Methods and systems for microbial detection using Raman spectroscopy

Abstract

Methods for detecting microorganisms in a sample are provided, the methods comprising measuring a Raman spectrum of the sample. In some embodiments, the measuring is automated. In some embodiments, the monitoring is continuous and / or non-invasive and / or non-destructive. Also provided are systems for detecting microorganisms in a sample, the systems comprising means for measuring a Raman spectrum of the sample. In some embodiments, the measuring is automated. In some embodiments, the monitoring is continuous and / or non-invasive and / or non-destructive. Also provided are methods and systems for assaying a test substance.

Description

[Technical Field]

[0001] The general inventive concept relates to the field of microbial detection, and more particularly to a method and system for automated detection of microorganisms using Raman spectroscopy.

[0002] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 63 / 380,446, filed October 21, 2022, U.S. Provisional Patent Application No. 63 / 380,455, filed October 21, 2022, U.S. Provisional Patent Application No. 63 / 380,461, filed October 21, 2022, U.S. Provisional Patent Application No. 63 / 380,494, filed October 21, 2022, U.S. Provisional Patent Application No. 63 / 380,505, filed October 21, 2022, and U.S. Provisional Patent Application No. 63 / 380,511, filed October 21, 2022, each of which is incorporated herein by reference in its entirety. [Background technology]

[0003] During the manufacturing of biologics, the entire process, as well as the resulting drug substance and drug product, must maintain established bioburden limits. Bioburden testing is routinely performed as in-process control (IPC) testing on the final product release of drug substances and non-sterile drug products. To ensure microbiological quality and sterility assurance, IPC testing monitors the bioburden of multiple unit operations in biologics manufacturing, such as production bioreactors, chromatographic purification, and filtration processes. Traditional microbiological testing is based on century-old technology using agar plates to enumerate microbial colony-forming units (CFUs). 1~2Traditional plate counting for microbial bioburden monitoring of in-process or final-product samples requires 2–5 days of incubation for quantitative enumeration. Traditional microbiological methods are manual, prone to cross-contamination, provide delayed and retroactive results, can be subjectively interpreted, have limited data traceability, and can result in data integrity issues. Current bioreactor specifications allow for low CFU levels (<5 CFU / 0.5 mL) to avoid false positives due to errors in compendial testing. To avoid unnecessary delays, manufacturing processes often risk advancing to the next unit operation while waiting for IPC bioburden test results. Ultimately, DS / DP release testing requirements place enormous demands on quality control laboratories, performing thousands of tests per year, creating a bottleneck for final product release.

[0004] Biologics manufacturers are under pressure to increase manufacturing efficiency to meet patient demand for life-saving treatments. Batch release testing for biologics can take up to 30 days, primarily due to the long time it takes to complete microbiological quality and sterility assurance tests such as mycoplasma, bioburden, sterility, and endotoxin. 3 Efforts in formulation of small molecule APIs have successfully incorporated multivariate statistical modeling and process analytical techniques (PAT) to enable measurement of both IPC (i.e., content uniformity) and final DP release testing (i.e., dissolution and assay), allowing product release within hours of manufacturing, effectively achieving real-time release. 4~5Real-time release testing (RTRT) is defined as "the ability to assess and assure in-process and / or final drug product quality based on process data, typically including a validated combination of measured raw material attributes and process controls." In biologics, efforts are underway to apply the same approach to eliminate IPC and reduce quality control (QC) drug substance and drug product release testing. Besides bioburden, common examples of IPC in bioreactor operations include glucose, viable cell density, and potency, all of which have been successfully measured with in-line PAT sensors. 6~14 Examples of spectroscopy-based PAT sensing for in-line testing of quality attributes required for drug substance and drug product release include protein and excipient concentrations, as well as protein-specific critical quality attributes such as charge, aggregation, glycosylation, or post-translational modification (PTM). 15~18 Recently, there has been increased interest in developing new methods to address IPC and release bioburden and sterility testing. 19~22 To date, these efforts have focused on developing assays that significantly reduce testing time, identify species, and are intended for either QC lab or near-line testing. Most of these tests are destructive, and they all require sample preparation (labeling, filtration, or other isolation) prior to testing. Recent efforts require centrifugation of the sample prior to Raman spectroscopy. 25

[0005] There remains a need for methods and systems for rapid, automated detection of microorganisms using Raman spectroscopy. Summary of the Invention

[0006] method Methods for detecting microorganisms A method for detecting microorganisms in a sample is provided, comprising measuring a Raman spectrum of the sample.

[0007] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0008] In some embodiments, the Raman spectrum is measured using a Raman spectrometer.

[0009] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0010] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0011] In some embodiments, the laser wavelength is 785 nm.

[0012] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill finish tank, or an incubator.

[0013] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0014] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0015] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0016] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0017] In some embodiments, the monitoring is automated.

[0018] In some embodiments, the monitoring is continuous.

[0019] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0020] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0021] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0022] In some embodiments, the method further comprises detecting fluorescence of the sample. In further embodiments, the means for detecting fluorescence is a fluorometer.

[0023] In some embodiments, the method further comprises detecting bioluminescence of the sample. In further embodiments, the means for detecting bioluminescence is a luminometer.

[0024] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0025] In some embodiments, the measurement of the Raman spectrum is automated.

[0026] In some embodiments, the Raman spectrum is subjected to data analysis.

[0027] In some embodiments, data analysis of the Raman spectra includes modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof. In some embodiments, the modeling is discriminant modeling.

[0028] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0029] In some embodiments, the sample comprises eukaryotic cells.

[0030] In some embodiments, the eukaryotic cell produces a protein, an antibody or fragment thereof, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0031] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0032] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0033] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0034] In some embodiments, the microorganism is actively growing.

[0035] Methods for continuous monitoring A method for continuously monitoring the presence of microorganisms in a sample is provided, comprising measuring a Raman spectrum of the sample.

[0036] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0037] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0038] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0039] In some embodiments, the Raman spectrum is measured using a Raman spectrometer.

[0040] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0041] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0042] In some embodiments, the laser wavelength is 785 nm.

[0043] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0044] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0045] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0046] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0047] In some embodiments, the monitoring is automated.

[0048] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0049] In a further embodiment, the sample has been contacted with an antibody to the microorganism.

[0050] In some embodiments, the sample has been contacted with a fluorescent or bioluminescent agent. In some embodiments, the method further comprises detecting the fluorescence or bioluminescence of the sample.

[0051] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0052] In some embodiments, the measurement of the Raman spectrum is automated.

[0053] In some embodiments, the Raman spectrum is subjected to data analysis.

[0054] In some embodiments, data analysis of the Raman spectra comprises discriminant modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof.

[0055] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0056] In some embodiments, the sample comprises eukaryotic cells.

[0057] In some embodiments, the eukaryotic cell produces a protein, an antibody, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0058] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0059] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0060] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0061] In some embodiments, the microorganism is actively growing.

[0062] Methods for Antibacterial Testing A method for assaying a test substance is provided that includes adding the test substance to a sample containing a microorganism and measuring a Raman spectrum of the sample.

[0063] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0064] In some embodiments, the Raman spectrum is measured using a Raman spectrometer.

[0065] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0066] Suitable laser wavelengths include, but are not limited to, wavelengths of 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0067] In some embodiments, the laser wavelength is 785 nm.

[0068] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0069] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0070] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0071] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0072] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0073] In some embodiments, the monitoring is automated.

[0074] In some embodiments, the monitoring is continuous.

[0075] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0076] In some embodiments, the test substance is a chemical compound, an adjuvant, an antibiotic, a bacteriophage, or a combination thereof.

[0077] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0078] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0079] In some embodiments, the method further comprises detecting fluorescence of the sample. In further embodiments, the means for detecting fluorescence is a fluorometer.

[0080] In some embodiments, the method further comprises detecting bioluminescence of the sample. In further embodiments, the means for detecting bioluminescence is a luminometer.

[0081] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0082] In some embodiments, the measurement of the Raman spectrum is automated.

[0083] In some embodiments, the Raman spectrum is subjected to data analysis.

[0084] In some embodiments, data analysis of the Raman spectra includes modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof. In some embodiments, the modeling is discriminant modeling.

[0085] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0086] In some embodiments, the sample comprises eukaryotic cells.

[0087] In some embodiments, the eukaryotic cell produces a protein, an antibody, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0088] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0089] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0090] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0091] In some embodiments, the microorganism is actively growing.

[0092] system System for detecting microorganisms A system for detecting microorganisms in a sample is provided, the system including means for measuring the Raman spectrum of the sample.

[0093] In some embodiments, the system further comprises a coherent light source.

[0094] In some embodiments, the means for measuring the Raman spectrum is a Raman spectrometer.

[0095] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0096] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0097] In some embodiments, the laser wavelength is 785 nm.

[0098] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0099] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0100] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0101] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0102] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0103] In some embodiments, the monitoring is automated.

[0104] In some embodiments, the monitoring is continuous.

[0105] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0106] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0107] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0108] In some embodiments, the system further comprises means for detecting fluorescence of the sample, hi further embodiments, the means for detecting fluorescence is a fluorometer.

[0109] In some embodiments, the system further comprises a means for detecting bioluminescence of the sample, hi further embodiments, the means for detecting bioluminescence is a luminometer.

[0110] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0111] In some embodiments, the measurement of the Raman spectrum is automated.

[0112] In some embodiments, the Raman spectrum is subjected to data analysis.

[0113] In some embodiments, data analysis of the Raman spectra includes modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof. In some embodiments, the modeling is discriminant modeling.

[0114] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0115] In some embodiments, the sample comprises eukaryotic cells.

[0116] In some embodiments, the eukaryotic cell produces a protein, an antibody or fragment thereof, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0117] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0118] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0119] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0120] In some embodiments, the microorganism is actively growing.

[0121] Systems for continuous monitoring Also provided is a system for continuously monitoring the presence of microorganisms in a sample, the system including means for measuring the Raman spectrum of the sample.

[0122] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0123] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0124] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0125] In some embodiments, the Raman spectrum is measured using a Raman spectrometer.

[0126] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0127] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0128] In some embodiments, the laser wavelength is 785 nm.

[0129] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0130] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0131] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0132] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0133] In some embodiments, the monitoring is automated.

[0134] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0135] In some embodiments, the system further comprises detecting fluorescence of the sample.

[0136] In a further embodiment, the sample has been contacted with an antibody to the microorganism.

[0137] In some embodiments, the sample has been contacted with a fluorescent or bioluminescent agent.

[0138] In some embodiments, the system further comprises means for detecting fluorescence of the sample, hi further embodiments, the means for detecting fluorescence is a fluorometer.

[0139] In some embodiments, the system further comprises a means for detecting bioluminescence of the sample, hi further embodiments, the means for detecting bioluminescence is a luminometer.

[0140] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0141] In some embodiments, the measurement of the Raman spectrum is automated.

[0142] In some embodiments, the Raman spectrum is subjected to data analysis.

[0143] In some embodiments, data analysis of the Raman spectra comprises discriminant modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof.

[0144] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0145] In some embodiments, the sample comprises eukaryotic cells.

[0146] In some embodiments, the eukaryotic cell produces a protein, an antibody, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0147] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0148] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0149] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0150] In some embodiments, the microorganism is actively growing.

[0151] System for Assaying Test Substances A system for assaying a test substance is provided, the system including means for measuring the Raman spectrum of a sample to which the test substance has been added.

[0152] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0153] In some embodiments, the Raman spectrum is measured using a Raman spectrometer.

[0154] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0155] Suitable laser wavelengths include, but are not limited to, wavelengths of 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0156] In some embodiments, the laser wavelength is 785 nm.

[0157] In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0158] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0159] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0160] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0161] In some embodiments, the Raman spectrum of the sample is monitored over time.

[0162] In some embodiments, the monitoring is automated.

[0163] In some embodiments, the monitoring is continuous.

[0164] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0165] In some embodiments, the test substance is a chemical compound, an adjuvant, an antibiotic, a bacteriophage, or a combination thereof.

[0166] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0167] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0168] In some embodiments, the system further comprises means for detecting fluorescence of the sample, hi further embodiments, the means for detecting fluorescence is a fluorometer.

[0169] In some embodiments, the system further comprises a means for detecting bioluminescence of the sample, hi further embodiments, the means for detecting bioluminescence is a luminometer.

[0170] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0171] In some embodiments, the measurement of the Raman spectrum is automated.

[0172] In some embodiments, the Raman spectrum is subjected to data analysis.

[0173] In some embodiments, data analysis of the Raman spectra includes discriminant modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof.

[0174] In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture vessel is diverted to a flow cell chamber for infrared analysis. In some embodiments, derivatization is performed before measuring the infrared spectrum of the sample.

[0175] In some embodiments, the sample comprises eukaryotic cells.

[0176] In some embodiments, the eukaryotic cell produces a protein, an antibody, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, or a combination thereof.

[0177] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0178] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0179] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample.

[0180] In some embodiments, the microorganism is actively growing. [Brief explanation of the drawings]

[0181] [Figure 1] A visual representation of each spiking test using offline parallel plating is shown. [Figure 2] Typical spectra (before and after pretreatment) of the bioreactor medium alone and the cell culture process are shown with the relevant wavenumber range highlighted. [Figure 3A] Principal component analysis (PCA) medium-only model overview. Figure 3A: Root mean squared error of calibration (RMSEC) and root mean squared error of cross-validation (RMSECV) vs. principal components (PC). Figure 3B: Q residuals vs. sample. Figure 3C: T2 vs. sample. Figure 3D: Score. [Figure 3B] Principal component analysis (PCA) medium-only model overview. Figure 3A: Root mean squared error of calibration (RMSEC) and root mean squared error of cross-validation (RMSECV) vs. principal components (PC). Figure 3B: Q residuals vs. sample. Figure 3C: T2 vs. sample. Figure 3D: Score. [Figure 3C] Principal component analysis (PCA) medium-only model overview. Figure 3A: Root mean squared error of calibration (RMSEC) and root mean squared error of cross-validation (RMSECV) vs. principal components (PC). Figure 3B: Q residuals vs. sample. Figure 3C: T2 vs. sample. Figure 3D: Score. [Figure 3D] Principal component analysis (PCA) medium-only model overview. Figure 3A: Root mean squared error of calibration (RMSEC) and root mean squared error of cross-validation (RMSECV) vs. principal components (PC). Figure 3B: Q residuals vs. sample. Figure 3C: T2 vs. sample. Figure 3D: Score. [Figure 4A] PCA cell culture only model summary. Figure 4A: RMSEC and RMSECV vs. PC. Figure 4B: Q residuals vs. sample. Figure 4C: T2 vs. sample. Figure 4D: Score. Figure 4E: Score. [Figure 4B] PCA cell culture only model summary. Figure 4A: RMSEC and RMSECV vs. PC. Figure 4B: Q residuals vs. sample. Figure 4C: T2 vs. sample. Figure 4D: Score. Figure 4E: Score. [Figure 4C] PCA cell culture only model summary. Figure 4A: RMSEC and RMSECV vs. PC. Figure 4B: Q residuals vs. sample. Figure 4C: T2 vs. sample. Figure 4D: Score. Figure 4E: Score. [Figure 4D] PCA cell culture only model summary. Figure 4A: RMSEC and RMSECV vs. PC. Figure 4B: Q residuals vs. sample. Figure 4C: T2 vs. sample. Figure 4D: Score. Figure 4E: Score. [Figure 4E] PCA cell culture only model summary. Figure 4A: RMSEC and RMSECV vs. PC. Figure 4B: Q residuals vs. sample. Figure 4C: T2 vs. sample. Figure 4D: Score. Figure 4E: Score. [Figure 5A] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5B]Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5C] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5D] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5E] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5F] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5G] Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 5H]Figure 5 shows an overview of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) cell culture model. Figure 5A: RMSEC and RMSECV vs. PC. Figure 5B: T2 vs. Sample. Figure 5C: Q Residuals vs. Sample. Figure 5D: Score. Figure 5E: Calibration Error. Figure 5F: Y Prediction Plot. Figure 5G: Receiver Operating Characteristic (ROC) Curve. Figure 5H: Sensitivity vs. Specificity. [Figure 6A] Figure 6 shows an overview of the k-nearest neighbor (KNN) cell culture model. Figure 6A: Calibration error. Figure 6B: Prediction plot. [Figure 6B] Figure 6 shows an overview of the k-nearest neighbor (KNN) cell culture model. Figure 6A: Calibration error. Figure 6B: Prediction plot. [Figure 7A] Figure 7A shows the contamination trajectories predicted using the OPLS-DA cell culture model for a non-contaminated production-scale batch (Figure 7A) and a contaminated reduced-scale batch (Figure 7B) of the calibration test sample set (CTSS). In the reduced-scale batch, contamination was detected by plating at t = 193 h and is indicated by a red star. The dashed line indicates the predicted Y value (YpredPS) limit of 0.35 in the predicted set that classifies a spectrum as borderline, while the solid line indicates the YPredPS limit of 0.65 that classifies a spectrum as contaminated. Data points for each sample are coded according to contaminated (black), uncontaminated (light gray), and equivocal (dark gray). [Figure 7B] Figure 7A shows the contamination trajectories predicted using the OPLS-DA cell culture model for a non-contaminated production-scale batch (Figure 7A) and a contaminated reduced-scale batch (Figure 7B) of the calibration test sample set (CTSS). In the reduced-scale batch, contamination was detected by plating at t = 193 h and is indicated by a red star. The dashed line indicates the predicted Y value (YpredPS) limit of 0.35 in the predicted set that classifies a spectrum as borderline, while the solid line indicates the YPredPS limit of 0.65 that classifies a spectrum as contaminated. Data points for each sample are coded according to contaminated (black), uncontaminated (light gray), and equivocal (dark gray). [Figure 8] 1 shows a table of test results demonstrating that the Raman method detects microbial contamination in bioreactors faster than conventional smear plate culture. [Figure 9A]Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 9B] Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 9C] Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 9D]Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 9E] Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 9F] Figure 9 shows an overview of the PLS-DA cell culture model for STELARA® (ustekinumab; Janssen) using the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Candida albicans, Bacillus cereus, Cutibacterium acnes, Bacillus subtilis, and Aspergillus brasiliensis. Figure 9A: RMSECV vs. PC. Figure 9B: T2 vs. sample. Figure 9C: Distance to model (DmodX) vs. sample. Figure 9D: Cumulative r-squared (R2Cum) and cumulative Q-squared index (Q2Cum) vs. PC. Figure 9E: Score. Figure 9F: Y-prediction plot. Data points for each sample are color-coded according to whether they are contaminated (black) or uncontaminated (gray). [Figure 10A]Figure 10A shows the predictions of the OPLS-DA contamination model in a CTSS with ambiguous transition spectra for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and control experiments from reactors from the remaining species in the calibration sample set (CSS). Figure 10A shows predicted values ​​(Ypred) vs. sample in the working set. Figure 10B shows the Ypred plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 10C shows the ROC plot. The AUC is 0.716. Data points for each sample are color-coded according to contaminated (black), uncontaminated (light gray), and ambiguous (dark gray). [Figure 10B] Figure 10A shows the predictions of the OPLS-DA contamination model in a CTSS with ambiguous transition spectra for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and control experiments from reactors from the remaining species in the calibration sample set (CSS). Figure 10A shows predicted values ​​(Ypred) vs. sample in the working set. Figure 10B shows the Ypred plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 10C shows the ROC plot. The AUC is 0.716. Data points for each sample are color-coded according to contaminated (black), uncontaminated (light gray), and ambiguous (dark gray). [Figure 10C]Figure 10A shows the predictions of the OPLS-DA contamination model in a CTSS with ambiguous transition spectra for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and control experiments from reactors from the remaining species in the calibration sample set (CSS). Figure 10A shows predicted values ​​(Ypred) vs. sample in the working set. Figure 10B shows the Ypred plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 10C shows the ROC plot. The AUC is 0.716. Data points for each sample are color-coded according to contaminated (black), uncontaminated (light gray), and ambiguous (dark gray). [Figure 11A] Figure 11A shows the predictions of the OPLS-DA contamination model in a CTSS with an unclear transition spectrum for STELARA® (ustekinumab; Janssen) using control experiments from reactors for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and the remaining species in the CSS. Figure 11A: YPred vs. sample. Figure 11B: YPrediction plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 11C: ROC plot. The AUC is 0.997. Data points for each sample are color-coded according to whether they are contaminated (black) or not (gray). [Figure 11B]Figure 11A shows the predictions of the OPLS-DA contamination model in a CTSS with an unclear transition spectrum for STELARA® (ustekinumab; Janssen) using control experiments from reactors for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and the remaining species in the CSS. Figure 11A: YPred vs. sample. Figure 11B: YPrediction plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 11C: ROC plot. The AUC is 0.997. Data points for each sample are color-coded according to whether they are contaminated (black) or not (gray). [Figure 11C] Figure 11A shows the predictions of the OPLS-DA contamination model in a CTSS with an unclear transition spectrum for STELARA® (ustekinumab; Janssen) using control experiments from reactors for the following test microbial species: Staphylococcus epidermidis, Escherichia coli, Bacillus cereus, Bacillus subtilis, and the remaining species in the CSS. Figure 11A: YPred vs. sample. Figure 11B: YPrediction plot. The dashed line indicates the YPredPS threshold of 0.35 for classifying a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65 for classifying a spectrum as contaminated. Figure 11C: ROC plot. The AUC is 0.997. Data points for each sample are color-coded according to whether they are contaminated (black) or not (gray). [Figure 12A] Figure 12A shows batch trajectory predictions for an uncontaminated, reduced-scale batch of Stelara® (ustekinumab; Janssen) and a contaminated, reduced-scale batch of CTSS containing Bacillus cereus (Figure 12B). In the small-scale batch, contamination was detected by plating at t = 46.5 h and is indicated by a star. The dashed line indicates the YPredPS threshold of 0.35, which classifies a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65, which classifies a spectrum as contaminated. Data points for each sample are color-coded according to contaminated (black), uncontaminated (light gray), and equivocal (dark gray). [Figure 12B]Figure 12A shows batch trajectory predictions for an uncontaminated, reduced-scale batch of Stelara® (ustekinumab; Janssen) and a contaminated, reduced-scale batch of CTSS containing Bacillus cereus (Figure 12B). In the small-scale batch, contamination was detected by plating at t = 46.5 h and is indicated by a star. The dashed line indicates the YPredPS threshold of 0.35, which classifies a spectrum as borderline, and the solid line indicates the YPredPS threshold of 0.65, which classifies a spectrum as contaminated. Data points for each sample are color-coded according to contaminated (black), uncontaminated (light gray), and equivocal (dark gray). DETAILED DESCRIPTION OF THE INVENTION

[0182] While the general inventive concept is susceptible to embodiment in many forms, specific embodiments thereof are shown in the drawings and described in detail herein, with the understanding that the present disclosure is to be considered as an exemplification of the principles of the general inventive concept. Accordingly, the general inventive concept is not intended to be limited to the specific embodiments shown herein.

[0183] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0184] The articles "a" and "an" are used herein to refer to one or to more than one (i.e., at least one) of the object of the article. By way of example, "a cell" means one cell or more than one cell.

[0185] As used herein, "about," when referring to measurable values ​​such as amounts and temporal durations, is meant to encompass variations of ±5%, preferably ±1%, and even more preferably ±0.1% from the specified value, as appropriate for practicing the disclosed methods.

[0186] As used herein, the term "coherent light source" means a light source that emits light waves that have the same frequency, wavelength, and are in phase or have a constant phase difference.

[0187] The terms "antibody" and "antibodies," as used herein, are intended to have broad meanings and include immunoglobulin molecules, including polyclonal antibodies, monoclonal antibodies, including murine, human, human-adapted, humanized, and chimeric monoclonal antibodies, antibody fragments, bispecific or multispecific antibodies, dimeric, tetrameric, or multimeric antibodies, and single-chain antibodies.

[0188] Immunoglobulins can be assigned to five major classes, namely, IgA, IgD, IgE, IgG, and IgM, depending on the amino acid sequence of the heavy chain constant domain. IgA and IgG are further subdivided into isotypes, IgA1, IgA2, IgG1, IgG2, IgG3, and IgG4. Antibody light chains of any vertebrate species can be assigned to one of two clearly distinct types, namely, kappa (κ) and lambda (λ), based on the amino acid sequence of their constant domain.

[0189] The term "antibody fragment" refers to a portion of an immunoglobulin molecule having a heavy chain and / or light chain antigen-binding site, for example, heavy chain complementarity determining regions (HCDRs) 1, 2, and 3, light chain complementarity determining regions (LCDRs) 1, 2, and 3, a heavy chain variable region (VH), or a light chain variable region (VL). Antibody fragments include a Fab fragment, which is a monovalent fragment consisting of the VL, VH, CL, and CH1 domains; an F(ab')2 fragment, which is a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region; an Fd fragment consisting of the VH and CH1 domains; an Fv fragment consisting of the VL and VH domains of one antibody arm; and a domain antibody (dAb) fragment consisting of the VH domain. Although VH and VL domains can be engineered and linked together via synthetic linkers to form a variety of single-chain antibody designs, the VH / VL domains pair intramolecularly, or, when the VH and VL domains are expressed as separate single-chain antibody constructs, pair intermolecularly to form a monovalent antigen-binding site, such as a single-chain Fv (scFv) or diabody. These are described, for example, in WO 1998 / 44001, WO 1988 / 01649, WO 1994 / 13804, and WO 1992 / 01047. These antibody fragments are obtained using techniques well known to those of skill in the art, and the fragments are screened for utility in the same manner as full-length antibodies.

[0190] The phrase "isolated antibody" refers to an antibody or antibody fragment that is substantially free of other antibodies having different antigenic specificities (e.g., an isolated antibody that specifically binds CD38 is substantially free of antibodies that specifically bind to antigens other than human CD38). However, an isolated antibody that specifically binds human CD38 may have cross-reactivity to other antigens, such as orthologs of human CD38, such as Macaca fascicularis (cynomolgus monkey) CD38. Moreover, an isolated antibody may be substantially free of other cellular material and / or chemicals.

[0191] A "humanized antibody" refers to an antibody in which the antigen-binding site is derived from a non-human species and the variable region framework is derived from human immunoglobulin sequences. Because humanized antibodies may contain substitutions within the framework regions, such frameworks may not be exact copies of expressed human immunoglobulin or germline gene sequences.

[0192] A "human antibody" refers to an antibody having heavy and light chain variable regions in which both the framework and antigen-binding site are derived from sequences of human origin. If the antibody contains a constant region, the constant region also is derived from sequences of human origin. A human antibody includes a heavy or light chain variable region "derived" from sequences of human origin when the variable region of the antibody is obtained from a system that uses human germline immunoglobulins or rearranged immunoglobulin genes. Such systems include human immunoglobulin gene libraries displayed on phage and transgenic non-human animals, such as mice, carrying human immunoglobulin loci described herein. A "human antibody" may contain amino acid differences when compared to human germline or rearranged immunoglobulin sequences, due, for example, to naturally occurring somatic mutations or the introduction of intentional substitutions in the framework or antigen-binding site. Typically, a human antibody is at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical in amino acid sequence to the amino acid sequence encoded by a human germline or rearranged immunoglobulin gene.

[0193] The isolated humanized antibody can be synthetic. Human antibodies are derived from human immunoglobulin sequences, but can be generated using systems such as phage display that incorporate synthetic CDRs and / or synthetic frameworks, or can be subjected to in vitro mutagenesis to improve the properties of the antibody, resulting in an antibody that does not naturally occur within the in vivo human antibody germline repertoire.

[0194] The term "recombinant antibody," as used herein, includes all antibodies that are prepared, expressed, created, or isolated by recombinant means, such as antibodies isolated from animals transgenic or transchromosomic for human immunoglobulin genes (e.g., mice) or hybridomas prepared therefrom, antibodies isolated from host cells transformed to express the antibody, antibodies isolated from recombinant combinatorial antibody libraries, and antibodies prepared, expressed, created, or isolated by any other means involving splicing human immunoglobulin gene sequences into other DNA sequences, or antibodies generated in vitro using Fab arm exchange (e.g., bispecific antibodies).

[0195] The term "monoclonal antibody," as used herein, refers to a preparation of antibody molecules of single molecular composition, displaying a single binding specificity and affinity for a particular epitope, or, in the case of bispecific monoclonal antibodies, dual binding specificities for two distinct epitopes.

[0196] The term "epitope," as used herein, refers to the portion of an antigen to which an antibody specifically binds. Epitopes usually consist of chemically active (e.g., polar, nonpolar, or hydrophobic) surface groups of moieties such as amino acids or polysaccharide side chains and may have specific three-dimensional structural characteristics and specific charge characteristics. Epitopes may be composed of contiguous and / or discontinuous amino acids that form a conformational spatial unit. In discontinuous epitopes, amino acids in different parts of the linear sequence of the antigen are brought into close proximity in three-dimensional space due to folding of the protein molecule.

[0197] The term "chimeric antigen receptor" or "CAR," as used herein, refers to a synthetic or recombinant receptor comprising an antigen-specific domain, a costimulatory domain, and an intracellular signaling domain. In some embodiments, the CAR further comprises an extracellular hinge or spacer region, a transmembrane domain, or a combination thereof. In some embodiments, the antigen-specific domain is an scFv.

[0198] The term "chimeric antigen receptor T cell" or "CAR-T" as used herein refers to a T cell that expresses a CAR.

[0199] As used herein, the term "bioreactor" generally refers to a device that supports a biologically active process (e.g., the cultivation of cells). Exemplary bioreactors include stainless steel stirred bioreactors, airlift reactors, and single-use bioreactors.

[0200] As used herein, the term "in-line" generally means that the measurement or determination is performed in real time by a probe placed within the vessel (e.g., within a bioreactor) and no sample collection is required.

[0201] In some embodiments of any of the compositions or methods described herein, ranges are intended to include every integer or fraction or value within the range.

[0202] Embodiments described herein as "comprising" one or more features may also be considered to disclose corresponding embodiments "consisting of" and / or "consisting essentially of" such features.

[0203] To date, the use of in-line Raman sensors for automated, non-invasive, and non-destructive detection of contamination by actively growing microorganisms directly in mammalian cell bioreactors has not been proposed. Provided herein is a method that eliminates the process intervention currently required to collect bioreactor samples for traditional smear plate analysis, with the added benefit of reducing the risk of daily cross-contamination in bioreactors. Furthermore, the versatility of Raman sensors to provide other process monitoring tests (PMTs) and IPC measurements lays the groundwork for eliminating bioreactor sampling entirely. Early in-house Raman method development, it was observed that when unintended microbial contamination occurred in bioreactors, the Raman probe sensed changes in the cell culture, both through changes in metabolic profiles (e.g., glucose consumption and lactate production) and through multivariate models that served as fingerprints of the biological process. These models were useful for identifying when the process deviated from expected nominal conditions. In this study, the ability of Raman multivariate analysis was further explored to determine whether it also has the specificity to distinguish between different types of microbial contamination (i.e., fast vs. slow and aerobic vs. anaerobic) and distinguish it from other process disturbances (such as under- or over-feed, pH, or gas control). The method developed by the inventors of the present invention has been demonstrated to be equivalent to or superior to compendial methods in terms of time to detection, detection of the presence of all test organisms, and can be validated with guidance documents to validate its suitability as an alternative to compendial methods. 23~24

[0204] Raman spectroscopy may include surface-enhanced Raman spectroscopy (SERS), spatially offset Raman spectroscopy (SORS), transmission Raman spectroscopy, and / or resonance Raman spectroscopy.

[0205] method Methods for detecting microorganisms A method for detecting microorganisms in a sample is provided, comprising measuring a Raman spectrum of the sample.

[0206] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0207] Methods for continuous monitoring A method for continuously monitoring the presence of microorganisms in a sample is provided, comprising measuring a Raman spectrum of the sample.

[0208] In some embodiments, the Raman spectrum of the sample is measured at set intervals, in some embodiments, the Raman spectrum is measured at random intervals, and in some embodiments, the Raman spectrum measurement is not interrupted.

[0209] In some embodiments, the Raman spectrum of the sample is monitored over a period of time. In further embodiments, the Raman spectrum of the sample is monitored at set intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored at random intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored continuously over a period of time. The period of time can be from about 0.1 to about 1000 hours, from about 0.1 to about 100 hours, from about 0.1 to about 10 hours, from about 0.1 to about 1 hour, or from about 0.1 to about 0.2 hours.

[0210] In some embodiments, the monitoring is automated.

[0211] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0212] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0213] Methods for Assaying Test Substances A method for assaying a test substance is provided that includes adding the test substance to a sample containing a microorganism and measuring a Raman spectrum of the sample.

[0214] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0215] Raman spectrometer In some embodiments, the Raman spectrum is measured using a Raman spectrometer. The Raman spectrometer may be commercially available, for example, a Kaiser Optical Systems RXN2 (Endress Hauser, Reinach, Switzerland). In some embodiments, the Raman spectrometer is constructed from commercially available individual components (e.g., lasers, spectrometers, detectors).

[0216] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0217] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0218] In some embodiments, the laser wavelength is 785 nm.

[0219] Sample growth conditions In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0220] The bioreactor may have any suitable volume that allows for the cultivation and growth of biological cells capable of producing a therapeutic protein (e.g., STELARA® (ustekinumab, Janssen)). For example, the volume of the bioreactor may be from about 0.5 liters (L) to about 25,000 L. In some embodiments, the volume of the bioreactor may be about 250 L or less. In some embodiments, the volume of the bioreactor may be about 0.5 liters (L) to about 250 L. In some embodiments, the volume of the bioreactor may be about 50 L or less. In some embodiments, the volume of the bioreactor may be about 1 L to about 50 L. In some embodiments, the volume of the bioreactor may be about 25 L or less. In some embodiments, the volume of the bioreactor may be about 1 L to about 25 L. In some embodiments, the volume of the bioreactor may be about 250 L or less. In some embodiments, the volume of the bioreactor may be about 100 L or less. In some embodiments, the volume of the bioreactor may be about 50 L or less. In some embodiments, the volume of the bioreactor can be about 25 L or less. In some embodiments, the volume of the bioreactor can be about 10 L or less. In some embodiments, the volume of the bioreactor can be about 5 L or less. In some embodiments, the volume of the bioreactor can be about 1 L or less. In some embodiments, the volume of the bioreactor can be about 1 L. In some embodiments, the volume of the bioreactor can be about 2 L. In some embodiments, the volume of the bioreactor can be about 5 L. In some embodiments, the volume of the bioreactor can be 1,000 L or about 1,000 L. In some embodiments, the volume of the bioreactor can be about 1,000 L to about 25,000 L. In some embodiments, the volume of the bioreactor can be about 10,000 L to about 25,000 L. In some embodiments, the volume of the bioreactor can be about 1,000 L. In some embodiments, the volume of the bioreactor can be about 2,000 L. In some embodiments, the volume of the bioreactor may be about 5,000 L.In some embodiments, the volume of the bioreactor can be about 10,000 L. In some embodiments, the volume of the bioreactor can be about 15,000 L. In some embodiments, the volume of the bioreactor can be about 25,000 L.

[0221] In some embodiments, the sample is at a temperature of about 2°C to about 40°C. In some embodiments, the sample is at a temperature of about 20°C to about 40°C. In some embodiments, the preferred temperature is about 25°C to about 37°C. In further embodiments, the sample is at a temperature of about 2°C to about 20°C. In yet further embodiments, the sample is at a temperature of about 2°C to about 10°C.

[0222] In some embodiments, the sample is grown for about 0.5 to about 14 days before measuring the Raman spectrum of the sample. In some embodiments, the sample is grown for about 0.5 to about 7 days before measuring the Raman spectrum of the sample. In further embodiments, the sample is grown for about 0.5 days, about 1 day, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, or about 7 days before measuring the Raman spectrum of the sample.

[0223] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0224] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0225] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm-1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0226] In some embodiments, the Raman spectrum of the sample is monitored over a period of time. In further embodiments, the Raman spectrum of the sample is monitored at set intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored at random intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored continuously over a period of time. The period of time can be from about 0.1 to about 1000 hours, from about 0.1 to about 100 hours, from about 0.1 to about 10 hours, from about 0.1 to about 1 hour, or from about 0.1 to about 0.2 hours.

[0227] In some embodiments, the monitoring is automated.

[0228] In some embodiments, the monitoring is continuous.

[0229] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0230] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0231] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0232] In some embodiments, the method further comprises detecting fluorescence of the sample. In further embodiments, the means for detecting fluorescence is a fluorometer.

[0233] In some embodiments, the method further comprises detecting bioluminescence of the sample. In further embodiments, the means for detecting bioluminescence is a luminometer.

[0234] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0235] In some embodiments, the measurement of the Raman spectrum is automated. In some embodiments, the automation is controlled by a central processing unit, e.g., a computer. In further embodiments, the automation is robotic.

[0236] Data analysis In some embodiments, the Raman spectrum is subjected to data analysis.

[0237] In some embodiments, data analysis of the Raman spectra includes discriminant modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof. In some embodiments, data analysis includes models for microorganisms including, but not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0238] In some embodiments, the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data, in some embodiments, the one or more models are regression models.

[0239] In some embodiments, the data analysis is performed by one or more computing devices.

[0240] In some embodiments, the Raman spectrometer is connected to one or more data processors and memories that can load and execute instructions by the data processing device. In some embodiments, the data processing device communicates via a network with one or more computing systems (e.g., servers, personal computers, tablets, IoT devices, mobile phones, dedicated control units, etc.) that can execute various algorithms or data analyses described elsewhere herein. The computing systems can also function to vary one or more operating parameters associated with the bioreactor. In some cases, the bioreactor can also have network connectivity such that it can communicate with one or more remote computing systems, which can in turn vary one or more operating parameters of the bioreactor.

[0241] Derivatization with DO In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0242] cell In some embodiments, the sample comprises eukaryotic cells. In further embodiments, the eukaryotic cells produce a therapeutic product. The therapeutic product may be released into the cell culture medium, where it may be collected. The methods provided herein allow for the detection of contamination.

[0243] In some embodiments, the eukaryotic cell produces a protein, an antibody or fragment thereof, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0244] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0245] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0246] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample. In some embodiments, the sample is not removed from the bioreactor, harvest tank, chromatography skid, chromatography column, ultrafiltration (UF) skid, diafiltration (DF) skid, UF / DF skid, fill finish tank, or incubator before measuring the Raman spectrum of the sample.

[0247] In some embodiments, the microorganism is actively growing.

[0248] Advantages of the methods provided herein include, but are not limited to, reduced risk of encountering false positive process samples due to cross-contamination in QC microbiology laboratories, reduced costs, and combinations thereof.

[0249] system System for detecting microorganisms A system for detecting microorganisms in a sample is provided, the system including means for measuring the Raman spectrum of the sample.

[0250] In some embodiments, the system further comprises a coherent light source.

[0251] Systems for continuous monitoring A system for continuously monitoring the presence of microorganisms in a sample is provided, the system including means for measuring the Raman spectrum of the sample.

[0252] In some embodiments, the system further comprises a coherent light source.

[0253] In some embodiments, the Raman spectrum of the sample is measured at set intervals, in some embodiments, the Raman spectrum is measured at random intervals, and in some embodiments, the Raman spectrum measurement is not interrupted.

[0254] In some embodiments, the Raman spectrum of the sample is monitored over a period of time. In further embodiments, the Raman spectrum of the sample is monitored at set intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored at random intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored continuously over a period of time. The period of time can be from about 0.1 to about 1000 hours, from about 0.1 to about 100 hours, from about 0.1 to about 10 hours, from about 0.1 to about 1 hour, or from about 0.1 to about 0.2 hours.

[0255] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0256] In some embodiments, the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

[0257] System for Assaying Test Substances A system for assaying a test substance is provided, the system including means for measuring the Raman spectrum of a sample to which the test substance has been added.

[0258] In some embodiments, the system further comprises a coherent light source.

[0259] Raman spectrometer In some embodiments, the Raman spectrum is measured using a Raman spectrometer. The Raman spectrometer may be commercially available, for example, a Kaiser Optical Systems RXN2 (Endress Hauser, Reinach, Switzerland). In some embodiments, the Raman spectrometer is constructed from commercially available individual components (e.g., lasers, spectrometers, detectors).

[0260] In some embodiments, the Raman spectrometer comprises a laser. In further embodiments, the laser is a multimode diode laser.

[0261] In some embodiments, the laser wavelength is 300 nm to 1200 nm, 350 nm to 1100 nm, 400 nm to 1100 nm, 400 nm to 1064 nm, 450 nm to 1064 nm, 500 nm to 1064 nm, 550 nm to 1064 nm, 600 nm to 1064 nm, 650 nm to 1064 nm, 700 nm to 1064 nm, 450 nm to 1100 nm, or 500 nm to 1100 nm. In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm.

[0262] In some embodiments, the laser wavelength is 785 nm.

[0263] Sample growth conditions In some embodiments, the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator.

[0264] The bioreactor may have any suitable volume that allows for the cultivation and growth of biological cells capable of producing a therapeutic protein (e.g., STELARA® (ustekinumab, Janssen)). For example, the volume of the bioreactor may be from about 0.5 liters (L) to about 25,000 L. In some embodiments, the volume of the bioreactor may be about 250 L or less. In some embodiments, the volume of the bioreactor may be about 0.5 liters (L) to about 250 L. In some embodiments, the volume of the bioreactor may be about 50 L or less. In some embodiments, the volume of the bioreactor may be about 1 L to about 50 L. In some embodiments, the volume of the bioreactor may be about 25 L or less. In some embodiments, the volume of the bioreactor may be about 1 L to about 25 L. In some embodiments, the volume of the bioreactor may be about 250 L or less. In some embodiments, the volume of the bioreactor may be about 100 L or less. In some embodiments, the volume of the bioreactor may be about 50 L or less. In some embodiments, the volume of the bioreactor can be about 25 L or less. In some embodiments, the volume of the bioreactor can be about 10 L or less. In some embodiments, the volume of the bioreactor can be about 5 L or less. In some embodiments, the volume of the bioreactor can be about 1 L or less. In some embodiments, the volume of the bioreactor can be about 1 L. In some embodiments, the volume of the bioreactor can be about 2 L. In some embodiments, the volume of the bioreactor can be about 5 L. In some embodiments, the volume of the bioreactor can be 1,000 L or about 1,000 L. In some embodiments, the volume of the bioreactor can be about 1,000 L to about 25,000 L. In some embodiments, the volume of the bioreactor can be about 10,000 L to about 25,000 L. In some embodiments, the volume of the bioreactor can be about 1,000 L. In some embodiments, the volume of the bioreactor can be about 2,000 L. In some embodiments, the volume of the bioreactor may be about 5,000 L.In some embodiments, the volume of the bioreactor can be about 10,000 L. In some embodiments, the volume of the bioreactor can be about 15,000 L. In some embodiments, the volume of the bioreactor can be about 25,000 L.

[0265] In some embodiments, the sample is at a temperature of about 2°C to about 40°C. In some embodiments, the sample is at a temperature of about 20°C to about 40°C. In some embodiments, the preferred temperature is about 25°C to about 37°C. In further embodiments, the sample is at a temperature of about 2°C to about 20°C. In yet further embodiments, the sample is at a temperature of about 2°C to about 10°C.

[0266] In some embodiments, the sample is grown for about 0.5 to about 14 days before measuring the Raman spectrum of the sample. In some embodiments, the sample is grown for about 0.5 to about 7 days before measuring the Raman spectrum of the sample. In further embodiments, the sample is grown for about 0.5 days, about 1 day, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, or about 7 days before measuring the Raman spectrum of the sample.

[0267] In some embodiments, the Raman spectrum of the sample is compared to the Raman spectrum of a control sample.

[0268] In some embodiments, the spectral range of the sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 200 to about 3600 cm -1 In a further embodiment, the spectral range of the sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the sample is from about 2800 to about 3100 cm -1 is.

[0269] In some embodiments, the spectral range of the control sample is from about 100 to about 3600 cm -1 In a further embodiment, the spectral range of the control sample is from about 200 to about 3600 cm-1 In a further embodiment, the spectral range of the control sample is from about 425 to about 1800 cm -1 In yet a further embodiment, the spectral range of the control sample is from about 2800 to about 3100 cm -1 is.

[0270] In some embodiments, the Raman spectrum of the sample is monitored over a period of time. In further embodiments, the Raman spectrum of the sample is monitored at set intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored at random intervals over a period of time. In still further embodiments, the Raman spectrum of the sample is monitored continuously over a period of time. The period of time can be from about 0.1 to about 1000 hours, from about 0.1 to about 100 hours, from about 0.1 to about 10 hours, from about 0.1 to about 1 hour, or from about 0.1 to about 0.2 hours.

[0271] In some embodiments, the monitoring is automated.

[0272] In some embodiments, the monitoring is continuous.

[0273] In some embodiments, the monitoring is non-invasive and / or non-destructive.

[0274] In some embodiments, the sample has been contacted with an antibody to the microorganism.

[0275] In some embodiments, the sample is contacted with a fluorescent or bioluminescent agent.

[0276] In some embodiments, the system further comprises means for detecting fluorescence of the sample, hi further embodiments, the means for detecting fluorescence is a fluorometer.

[0277] In some embodiments, the system further comprises a means for detecting bioluminescence of the sample, hi further embodiments, the means for detecting bioluminescence is a luminometer.

[0278] In some embodiments, the microorganism includes, but is not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. In some embodiments, the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0279] In some embodiments, the measurement of the Raman spectrum is automated. In some embodiments, the automation is controlled by a central processing unit, e.g., a computer. In further embodiments, the automation is robotic.

[0280] Data analysis In some embodiments, the Raman spectrum is subjected to data analysis.

[0281] In some embodiments, data analysis of the Raman spectra includes discriminant modeling performed using principal component analysis-X (PCA-X), orthogonal partial least squares (OPLS), k-nearest neighbor (KNN), orthogonal partial least squares discriminant analysis (OPLS-DA), partial least squares discriminant analysis (PLS-DA), or a combination thereof. In some embodiments, data analysis includes models for microorganisms including, but not limited to, Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

[0282] In some embodiments, the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data, in some embodiments, the one or more models are regression models.

[0283] In some embodiments, the data analysis is performed by one or more computing devices.

[0284] In some embodiments, the Raman spectrometer is connected to one or more data processors and memories that can load and execute instructions by the data processing device. In some embodiments, the data processing device communicates via a network with one or more computing systems (e.g., servers, personal computers, tablets, IoT devices, mobile phones, dedicated control units, etc.) that can execute various algorithms or data analyses described elsewhere herein. The computing systems can also function to vary one or more operating parameters associated with the bioreactor. In some cases, the bioreactor can also have network connectivity such that it can communicate with one or more remote computing systems, which can in turn vary one or more operating parameters of the bioreactor.

[0285] Derivatization with DO In some embodiments, the method further comprises incubation with DO for assessment of microbial viability. In further embodiments, the sample from the cell culture container is diverted to a flow cell chamber for Raman analysis. In some embodiments, derivatization is performed before measuring the Raman spectrum of the sample.

[0286] cell In some embodiments, the sample comprises eukaryotic cells. In further embodiments, the eukaryotic cells produce a therapeutic product. The therapeutic product may be released into the cell culture medium, where it may be collected. The systems provided herein allow for the detection of contamination.

[0287] In some embodiments, the eukaryotic cell produces a protein, an antibody or fragment thereof, a duobody, a receptor, a chimeric antigen receptor, a glycoprotein, a viral vector, or a combination thereof.

[0288] In some embodiments, the eukaryotic cell is a mouse cell, a Chinese hamster ovary (CHO) cell, or a human cell. In some embodiments, the cell is a T cell or a B cell. In some embodiments, the mouse cell is a mouse Sp2 / 0 cell. In some embodiments, the cell is a HEK293F cell. In some embodiments, the cell is a PER.C6 cell.

[0289] In some embodiments, the eukaryotic cell is a chimeric antigen receptor T cell (CAR-T cell).

[0290] In some embodiments, the sample is not centrifuged before measuring the Raman spectrum of the sample. In some embodiments, the sample is not removed from the bioreactor, harvest tank, chromatography skid, chromatography column, ultrafiltration (UF) skid, diafiltration (DF) skid, UF / DF skid, fill finish tank, or incubator before measuring the Raman spectrum of the sample.

[0291] In some embodiments, the microorganism is actively growing.

[0292] Advantages of the systems provided herein include, but are not limited to, reduced risk of encountering false positive process samples due to cross-contamination in QC microbiology laboratories, reduced costs, and combinations thereof. [Example]

[0293] Example 1: Spiking test Materials and Methods Culture preparation and plating method of test microorganisms Test microorganisms (Biomerieux, USA, Bioball or equivalent (Remel Quanti-cult plus)) were resuspended according to the manufacturer's instructions. A target suspension of 100 CFU or less per 0.1 mL was prepared to perform the designated inoculum test.

[0294] Target colony-forming units (CFU) were confirmed by triplicate smear plating of 0.1 mL aliquots of each test organism suspension onto individual pre-spiked TSA plates. Plates were incubated aerobically or anaerobically at 30–35°C for 1–3 days as specified, and the spiked feed counts for each bioreactor were determined.

[0295] Bioreactor samples were collected at least daily unless otherwise noted, and 0.5 mL smear plates were performed in triplicate using a 1 μL inoculation loop or equivalent. PBS-only smear plates served as a negative control. Samples were incubated at 35–37°C and examined daily to determine CFU.

[0296] Bioreactor operation The miniature bioreactors were either 1.5 L or 3.0 L in size. Each bioreactor was equipped with a pH probe, a dissolved oxygen (DO) probe, and a Raman probe. The temperature was controlled at 36.5°C. The DO set point was 40%. No pH control was performed. All tests used an in-house basal medium specialized for monoclonal antibody processes. The mouse Sp2 / 0 cell line was used in the cell culture reactors.

[0297] After allowing temperature and DO to stabilize for a minimum of 6 hours, the media-only bioreactors were spiked with low and high CFU targets (5 CFU, 50 CFU). After stabilization, the reactors were inoculated with 0.1 mL of the test organism suspension from welded inoculation bottles or inoculation bags (the suspension was diluted with 10 mL of media) into each designated bioreactor, and the time of spiking was recorded.

[0298] Inoculate the mouse cell line into the cell culture bioreactor at a target density of 0.3-0.5 x 10 cells. 6The cells were inoculated at 1000 cells / mL. Cell culture was allowed to grow for at least 24 hours before spiking the microorganisms. The bioreactor was then inoculated with the test microorganism suspension and the time of spiking was recorded. A low CFU target was applied to three organisms, while two organisms were spiked at a higher level to achieve the required growth.

[0299] Raman spectroscopy Raman data were collected on a Kaiser Optical Systems RXN2 (Endress Hauser) equipped with a runtime HMI operating system (version 5), a 785 nm excitation laser, and a CCD camera maintained at 40 °C. A fiber optic cable containing excitation and collection fibers was attached to the bioreactor. Acquisition parameters were 10 s exposure time per scan, 75 accumulations, and a total collection time of 12.5 min. Each Raman spectrum included a 100–3425 cm -1 The spectral region was included.

[0300] Data analysis Derivative, normalization, and wavelength selection (425-1800, 2800-3100 cm -1 Spectra were preprocessed using a chromatographic method. Wavelength selection included removing peaks due to optical window materials and regions without Raman information, as documented by the instrument supplier. Data were separated into a calibration sample set (CSS) and a calibration test set (CTS). Models were developed using CSS using only known uncontaminated and contaminated spectra. Discriminant modeling was performed using PCA-X, OPLS, and KNN with SIMCA version 15.0, SIMCA version 14.1, or Eigenvector PLS_Toolbox version 8.9.2. Data analysis consisted of models of the tested microorganisms.

[0301] Eigenvector (Eigenvector Research, Manson, WA) constructed models using filtering and despiking, extended multiplicative signal correction (EMSC), and mean centering. SIMCA used first derivatives, 31-point smoothing, and standard normal variates (SNV).

[0302] Raman and offline plating were compared by non-parametric Wilcoxon test.

[0303] Experimental design The test was defined as a test microorganism and a spiked target (see Figure 1). A set of four miniature bioreactors was intentionally spiked with different organisms representing slow- and fast-growing, anaerobic, and aerobic organisms according to Tables 1-2. One reactor served as a control and was not spiked with any microorganisms. The remaining three bioreactors were spiked. Triplicate smear plating of daily bioreactor samples, daily visual inspection for turbidity, and process parameters were recorded. Organisms were selected based on compendial methods. One set of experiments focused on a media-only bioreactor, and the second set was a cell culture bioreactor where the process was run to simulate the first few days of a perfusion-based process. Low and high CFU targets were used for the media-only reactor, and the low CFU target was used for the cell culture reactor to establish the lower limit of detection.

[0304] [Table 1]

[0305] [Table 2]

[0306] result Currently, daily bioreactor samples must be collected on the process floor and transported to a QC microbiology group for smear or pour plate analysis. The time between sample collection and actual testing can be up to 6 hours, and multiple agar plates may be required, which are prone to cross-contamination and require at least an overnight incubation before an initial CFU count can be detected. The in-line Raman method described in this study for monitoring bioreactor microbial contamination addresses these limitations of traditional plating.

[0307] The results of the parallel study presented here met all of the acceptance criteria outlined in Table 9 below, demonstrating that the in-line Raman method allows for continuous, automated, noninvasive, and nondestructive direct detection of actively growing microbial contamination in mammalian cell culture bioreactors compared to the currently used daily conventional plating method. The Raman method successfully detected the presence of all test microorganisms spiked into the media-only and cell culture bioreactors and recovered by parallel smear plating. Furthermore, the Raman method described in this study allows for improved data integrity and reduced time to detection compared to conventional plate count CFU results, while providing call equivalence in monitoring bioreactor microbial contamination.

[0308] A visual representation of each spiking test using offline parallel plating is shown in Figure 1.

[0309] Typical spectra of the bioreactor medium alone and the cell culture process (before and after pretreatment) are shown in Figure 2, highlighting the relevant wavenumber range.

[0310] An overview of the principal component analysis (PCA) medium-only model is shown in Figures 3A-3D.

[0311] An overview of the PCA cell culture only model is shown in Figures 4A-4E.

[0312] An overview of the orthogonal partial least squares discriminant analysis (OPLS-DA) cell culture model is shown in Figures 5A-5H.

[0313] An overview of the k-nearest neighbor (KNN) cell culture model is shown in Figures 6A-6B.

[0314] The contamination trajectories predicted using the OPLS-DA cell culture model for A) a non-contaminated production-scale batch and B) a contaminated reduced-scale batch of CTSS are shown in Figure 7.

[0315] The results of the successful Raman bioreactor microbial contamination monitoring test are summarized in Figure 8.

[0316] Results of studies with STELARA® (ustekinumab; Janssen) are summarized in Figures 9A-12B. Figures 9A-9F show an overview of the PLS-DA cell culture model of STELARA® (ustekinumab; Janssen) with several test microbial species.

[0317] Exemplary methods for making STELARA® (ustekinumab; Janssen) are set forth in U.S. Patent Application Publication No. 2020 / 0291107 or U.S. Patent Application Publication No. 2023 / 0038355, each of which is incorporated by reference in its entirety.

[0318] Predictions of the OPLS-DA contamination model in a CTSS with an unclear transition spectrum are shown in Figures 10A-10C for STELARA® (ustekinumab; Janssen) using several microbial species.

[0319] Predictions of the OPLS-DA contamination model for STELARA® (ustekinumab; Janssen) with several microbial species in CTSS without ambiguous transition spectra are shown in Figures 11A-11C.

[0320] Batch trajectory predictions for Stelara® (ustekinumab; Janssen) are shown in Figures 12A-B for A) an uncontaminated reduced-scale batch and B) a contaminated reduced-scale batch of CTSS containing Bacillus cereus.

[0321] In this study, a novel Raman method is presented that demonstrates in-line, continuous, automated, non-invasive, and non-destructive direct detection of actively growing microbial contamination in mammalian cell culture bioreactors, with equivalence of results compared to currently used daily conventional plating methods, but without the current limitations imposed by conventional sampling interventions.

[0322] [Table 3]

[0323] [Table 4] Cell culture: mouse Sp2 / 0 cell line

[0324] [Table 5] Cell culture: mouse Sp2 / 0 cell line

[0325] [Table 6]

[0326] [Table 7]

[0327] [Table 8]

[0328] [Table 9]

[0329] Embodiment The following exemplary embodiments further describe optional aspects of the technology of the present disclosure and are part of the detailed description. Although these exemplary embodiments are described in a format substantially similar to claims (each with a numerical designation followed by a capital letter), they are not technical claims of this application. The following exemplary embodiments are referenced to each other in a dependent relationship as "embodiments" instead of "claims." 1A. A method for detecting microorganisms in a sample, comprising measuring a Raman spectrum of the sample. 2A. The method of embodiment 1A, wherein the sample is exposed to a coherent light source prior to measuring the Raman spectrum of the sample. 3A. The method of embodiment 1A, wherein the Raman spectrum is measured using a Raman spectrometer. 4A. The method of embodiment 3A, wherein the Raman spectrometer comprises a laser. 5A. The method of embodiment 4A, wherein the laser is a multimode diode laser. 6A. The method of embodiment 4A or embodiment 5A, wherein the laser wavelength is 300 nm to 1200 nm. 7A. The method of embodiment 6A, wherein the laser wavelength is 785 nm. 8A. The method of any one of embodiments 1A-7A, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 9A. The method of any one of embodiments 1A-8A, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 10A. The method of any one of embodiments 1A-9A, wherein the Raman spectrum of the sample is monitored over time. 11A. The method of any one of embodiments 1A-10A, wherein the monitoring is continuous. 12A. The method of any one of embodiments 1A to 11A, wherein the monitoring is non-invasive and / or non-destructive. 13A. The method of any one of embodiments 1A to 12A, wherein the sample has been contacted with an antibody to a microorganism. 14A. The method of any one of embodiments 1A-13A, wherein the sample is contacted with a fluorescent or bioluminescent agent. 15A. The method of any one of embodiments 1A-14A, further comprising detecting fluorescence or bioluminescence of the sample. 16A. The method of any one of embodiments 1A-15A, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. 17A. The method of any one of embodiments 1A to 16A, wherein measuring the Raman spectrum is automated. 18A. The method of any one of embodiments 1A to 17A, wherein the Raman spectrum is subjected to data analysis. 19A. The method of any one of embodiments 1A-18A, wherein the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data. 20A. The method of any one of embodiments 1A-19A, wherein one or more models are regression models. 21A. The method of any one of embodiments 1A to 20A, wherein the data analysis of the Raman spectrum comprises modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 22A. The method of any one of embodiments 1A-21A, wherein the microorganism is actively growing. 1B. A method for continuously monitoring the presence of microorganisms in a sample, comprising measuring a Raman spectrum of the sample. 2B. The method of embodiment 1B, wherein the Raman spectrum of the sample is monitored over time. 3B. The method of embodiment 1B or embodiment 2B, wherein the monitoring is non-invasive and / or non-destructive. 4B. The method of any one of embodiments 1B-3B, wherein the sample is exposed to a coherent light source prior to measuring the Raman spectrum of the sample. 5B. The method of any one of embodiments 1B-4B, wherein the Raman spectrum is measured using a Raman spectrometer. 6B. The method of any one of embodiments 1B-5B, wherein the Raman spectrometer comprises a laser. 7B. The method of embodiment 6B, wherein the laser is a multimode diode laser. 8B. The method of embodiment 6B or embodiment 7B, wherein the laser wavelength is 300 nm to 1200 nm. 9B. The method of embodiment 8B, wherein the laser wavelength is 785 nm. 10B. The method of any one of embodiments 1B-9B, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 11B. The method of any one of embodiments 1B-10B, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 12B. The method of any one of embodiments 1B to 11B, wherein the sample has been contacted with an antibody to the microorganism. 13B. The method of any one of embodiments 1B-12B, wherein the sample is contacted with a fluorescent or bioluminescent agent. 14B. The method of any one of embodiments 1B-13B, further comprising detecting fluorescence or bioluminescence of the sample. 15B. The method of any one of embodiments 1B-14B, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. 16B. The method of any one of embodiments 1B to 15B, wherein measuring the Raman spectrum is automated. 17B. The method of any one of embodiments 1B to 16B, wherein the Raman spectrum is subjected to data analysis. 18B. The method of any one of embodiments 1B to 17B, wherein the data analysis includes generating, based on the obtained Raman spectral data, one or more models correlating the level of microorganisms with the obtained Raman spectral data. 19B. The method of any one of embodiments 1B-18B, wherein one or more models are regression models. 20B. The method of any one of embodiments 1B-19B, wherein the data analysis of the Raman spectra comprises discriminant modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 21B. The method of any one of embodiments 1B-20B, wherein the microorganism is actively growing. 1C. A method for assaying a test substance, comprising adding the test substance to a sample containing a microorganism and measuring a Raman spectrum of the sample. 2C. The method of embodiment 1C, wherein the Raman spectrum is measured using a Raman spectrometer. 3C. The method of embodiment 2C, wherein the Raman spectrometer comprises a laser. 4C. The method of embodiment 3C, wherein the laser is a multimode diode laser. 5C. The method of embodiment 3C or embodiment 4C, wherein the laser wavelength is 300 nm to 1200 nm. 6C. The method of embodiment 5C, wherein the laser wavelength is 785 nm. 7C. The method of any one of embodiments 1C-6C, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 8C. The method of any one of embodiments 1C-7C, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 9C. The method of any one of embodiments 1C-8C, wherein the Raman spectrum of the sample is monitored over time. 10C. The method of any one of embodiments 1C-9C, wherein the monitoring is continuous. 11C. The method of any one of embodiments 1C to 10C, wherein the monitoring is non-invasive and / or non-destructive. 12C. The method of any one of embodiments 1C-11C, wherein the test substance is a chemical compound, an adjuvant, an antibiotic, a bacteriophage, or a combination thereof. 13C. The method of any one of embodiments 1C to 12C, wherein the sample has been contacted with an antibody to the microorganism. 14C. The method of any one of embodiments 1C to 13C, wherein the sample is contacted with a fluorescent or bioluminescent agent. 15C. The method of any one of embodiments 1C to 14C, further comprising detecting fluorescence or bioluminescence of the sample. 16C. The method of any one of embodiments 1C-15C, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, or Bacillus cereus. 17C. The method of any one of embodiments 1C to 16C, wherein the measurement of the Raman spectrum is automated. 18C. The method of any one of embodiments 1C to 17C, wherein the Raman spectrum is subjected to data analysis. 19C. The method of any one of embodiments 1C to 18C, wherein the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data. 20C. The method of any one of embodiments 1C-19C, wherein one or more models are regression models. 21C. The method of any one of embodiments 1C to 20C, wherein the data analysis of the Raman spectra comprises discriminant modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 22C. The method of any one of embodiments 1C to 21C, wherein the microorganism is actively growing. 1D. A system for detecting microorganisms in a sample, the system comprising means for measuring a Raman spectrum of the sample. 2D. The system of embodiment 1D, further comprising a coherent light source. 3D. The system of embodiment 1D, wherein the means for measuring the Raman spectrum is a Raman spectrometer. 4D. The system of embodiment 3D, wherein the Raman spectrometer comprises a laser. 5D. The system of embodiment 4D, wherein the laser is a multimode diode laser. 6D. The system of embodiment 4D or 5D, wherein the laser wavelength is between 300 nm and 1200 nm. 7D. The system of embodiment 6D, wherein the laser wavelength is 785 nm. 8D. The system of any one of embodiments 1D-7D, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 9D. The system of any one of embodiments 1D-8D, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 10D. The system of any one of embodiments 1D-9D, wherein the Raman spectrum of the sample is monitored over time. 11D. The system of any one of embodiments 1D-10D, wherein the monitoring is continuous. 12D. The system of any one of embodiments 1D-11D, wherein the monitoring is non-invasive and / or non-destructive. 13D. The system of any one of embodiments 1D-12D, wherein the sample is contacted with an antibody to a microorganism. 14D. The system of any one of embodiments 1D-13D, wherein the sample is contacted with a fluorescent or bioluminescent agent. 15D. The system of any one of embodiments 1D-14D, further comprising detecting fluorescence or bioluminescence of the sample. 16D. The system of any one of embodiments 1D-15D, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. 17D. The system of any one of embodiments 1D-16D, wherein the measurement of the Raman spectrum is automated. 18D. The system of any one of embodiments 1D-17D, wherein the Raman spectrum is subjected to data analysis. 19D. The system of any one of embodiments 1D-18D, wherein the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data. 20D. The system of any one of embodiments 1D-19D, wherein one or more models are regression models. 21D. The system of any one of embodiments 1D-20D, wherein the data analysis of the Raman spectra includes modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 22D. The system of any one of embodiments 1D-21D, wherein the microorganism is actively growing. 1E. A system for continuously monitoring the presence of microorganisms in a sample, the system including means for measuring the Raman spectrum of the sample. 2E. The system of embodiment 1E, wherein the Raman spectrum of the sample is monitored over time. 3E. The system of embodiment 1E or embodiment 2E, wherein the monitoring is non-invasive and / or non-destructive. 4E. The system of any one of embodiments 1E-3E, wherein the sample is exposed to a coherent light source prior to measuring the Raman spectrum of the sample. 5E. The system of any one of embodiments 1E-4E, wherein the Raman spectrum is measured using a Raman spectrometer. 6E. The system of any one of embodiments 1E-5E, wherein the Raman spectrometer comprises a laser. 7E. The system of embodiment 6E, wherein the laser is a multimode diode laser. 8E. The system of embodiment 6E or embodiment 7E, wherein the laser wavelength is 300 nm to 1200 nm. 9E. The system of embodiment 8E, wherein the laser wavelength is 785 nm. 10E. The system of any one of embodiments 1E-9E, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 11E. The system of any one of embodiments 1E-10E, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 12E. The system of any one of embodiments 1E to 11E, wherein the sample is contacted with an antibody to a microorganism. 13E. The system of any one of embodiments 1E to 12E, wherein the sample is contacted with a fluorescent or bioluminescent agent. 14E. The system of any one of embodiments 1E-13E, further comprising detecting fluorescence or bioluminescence of the sample. 15E. The system of any one of embodiments 1E-14E, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus. 16E. The system of any one of embodiments 1E-15E, wherein measuring the Raman spectrum is automated. 17E. The system of any one of embodiments 1E-16E, wherein the Raman spectrum is subjected to data analysis. 18E. The system of any one of embodiments 1E to 17E, wherein the data analysis includes generating one or more models based on the obtained Raman spectral data that correlate the level of microorganisms with the obtained Raman spectral data. 19E. The system of any one of embodiments 1E-18E, wherein one or more models are regression models. 20E. The system of any one of embodiments 1E-19E, wherein data analysis of the Raman spectra includes discriminant modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 21E. The system of any one of embodiments 1E-20E, wherein the microorganism is actively growing. 1F. A system for assaying a test substance, the system comprising means for measuring the Raman spectrum of a sample to which the test substance has been added. 2F. The system of embodiment 1F, further comprising a coherent light source. 3F. The system of embodiment 1F, wherein the means for measuring the Raman spectrum is a Raman spectrometer. 4F. The system of embodiment 3F, wherein the Raman spectrometer comprises a laser. 5F. The system of embodiment 4F, wherein the laser is a multimode diode laser. 6F. The system of embodiment 4F or embodiment 5F, wherein the laser wavelength is 300 nm to 1200 nm. 7F. The system of embodiment 6F, wherein the laser wavelength is 785 nm. 8F. The system of any one of embodiments 1F-7F, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill-finish tank, or an incubator. 9F. The system of any one of embodiments 1F-8F, wherein the Raman spectrum of the sample is compared to the Raman spectrum of a control sample. 10F. The system of any one of embodiments 1F-9F, wherein the Raman spectrum of the sample is monitored over time. 11F. The system of any one of embodiments 1F-10F, wherein the monitoring is continuous. 12F. The system of any one of embodiments 1F-11F, wherein the monitoring is non-invasive and / or non-destructive. 13F. The system of any one of embodiments 1F-12F, wherein the sample is contacted with an antibody to a microorganism. 14F. The system of any one of embodiments 1F-13F, wherein the sample is contacted with a fluorescent or bioluminescent agent. 15F. The system of any one of embodiments 1F-14F, further comprising detecting fluorescence or bioluminescence of the sample. 16F. The system of any one of embodiments 1F-15F, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, or Bacillus cereus. 17F. The system of any one of embodiments 1F-16F, wherein measuring the Raman spectrum is automated. 18F. A system according to any one of claims 1F to 17F, wherein the Raman spectrum is subjected to data analysis. 19F. The system of any one of embodiments 1F-18F, wherein the data analysis includes generating, based on the obtained Raman spectral data, one or more models correlating the level of microorganisms with the obtained Raman spectral data. 20F. The system of any one of embodiments 1F-19F, wherein one or more models are regression models. 21F. The system of any one of embodiments 1F-20F, wherein the data analysis of the Raman spectra includes modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof. 22F. The system of any one of embodiments 1F-21F, wherein the microorganisms are actively growing.

[0330] References 1.USP-NF <61> Microbiological Examination of Nonsterile Products Microbial Enumeration Tests. 2.2.6.12. MICROBIOLOGICAL EXAMINATION OF NON-STERILE PRODUCTS (TOTAL VIABLE AEROBIC COUNT). Pharmacopeia, E., Ed. 3.Rapid Detection of Bateria and Viruses in Bioprocess samples:Justification,regulation,requirments and technologies-How can industry achieve broad adoption?;Biophorum Operations Group:2019。 4.Vargas,J.M.;Nielsen,S.;Cardenas,V.;Gonzalez,A.;Aymat,E.Y.;Almodovar,E.;Classe,G.;Colon,Y.Sanchez,E.;Romanach,R.J.,Process analytical technology in continuous manufacturing of a commercial pharmaceutical product.International Journal of Pharmaceutics 2018,538(1),167-178。 5.Krogmeier,S.;Pritchard,J.;Dokou,E.;Miles,S.;Connelly,G.;Medendorp,J.;Bourland,M.;Swinney,K.,Chapter 16-Orkambi:a continuous manufacturing approach to process development at Vertex.In How to Design and Implement Powder-To-Tablet Continuous Manufacturing Systems,Muzzio,F.J.;Oka,S.,Eds.Academic Press:2022;pp 383-396。 6.Abu-Absi,N.R.;Kenty,B.M.;Cuellar,M.E.;Borys,M.C.;Sakhamuri,S.;Strachan,D.J.;Hausladen,M.C.;Li,Z.J.,Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe.Biotechnol Bioeng 2011,108(5),1215-21。 7.Berry,B.;Moretto,J.;Matthews,T.;Smelko,J.;Wiltberger,K.,Cross-scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis.Biotechnol Prog 2015,31(2),566-77。 8.Mehdizadeh,H.;Lauri,D.;Karry,K.M.;Moshgbar,M.;Procopio-Melino,R.;Drapeau,D.,Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.Biotechnol Prog 2015,31(4),1004-13。 9.Sellick,C.A.;Hansen,R.;Jarvis,R.M.;Maqsood,A.R.;Stephens,G.M.;Dickson,A.J.;Goodacre,R.,Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics.Biotechnol Bioeng 2010,106(3),432-42。 10.Helgers,H.;Schmidt,A.;Lohmann,L.J.;Vetter,F.L.;Juckers,A.;Jensch,C.;Mouellef,M.;Zobel-Roos,S.;Strube,J.,Towards Autonomous Operation by Advanced Process Control-Process Analytical Technology for Continuous Biologics Antibody Manufacturing.Processes 2021,9(1),172。 11.Moore,B.;Sanford,R.;Zhang,A.,Case study:The characterization and implementation of dielectric spectroscopy(biocapacitance)for process control in a commercial GMP CHO manufacturing process.Biotechnology Progress 2019,35(3),e2782。 12.Reyes,S.J.;Durocher,Y.;Pham,P.L.;Henry,O.,Modern Sensor Tools and Techniques for Monitoring,Controlling,and Improving Cell Culture Processes.Processes 2022,10(2),189。 13.Zhang,A.;Tsang,V.L.;Moore,B.;Shen,V.;Huang,Y.-M.;Kshirsagar,R.;Ryll,T.,Advanced process monitoring and feedback control to enhance cell culture process production and robustness.Biotechnology and Bioengineering 2015,112(12),2495-2504。 14.Andre,S.;Cristau,L.S.;Gaillard,S.;Devos,O.;Calvosa,E.;Duponchel,L.,In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy.Analytica Chimica Acta 2015,892,148-152。 15.Wei,B.;Woon,N.;Dai,L.;Fish,R.;Tai,M.;Handagama,W.;Yin,A.;Sun,J.;Maier,A.;McDaniel,D.;Kadaub,E.;Yang,J.;Saggu,M.;Woys,A.;Pester,O.;Lambert,D.;Pell,A.;Hao,Z.;Magill,G.;Yim,J.;Chan,J.;Yang,L.;Macchi,F.;Bell,C.;Deperalta,G.;Chen,Y.,Multi-attribute Raman spectroscopy(MARS)for monitoring product quality attributes in formulated monoclonal antibody therapeutics.MAbs 2022,14(1),2007564。 16.Hu,Q.;Jiang,B.;Liu,D.;Tang,X.;Daly,T.;Shameem,M.,Chapter 14:Practical Considerations in High Concentration Formulation Development for Monoclonal Antibody Drug Products.In Development of Biopharmaceutical Drug-Device Products,Jameel,F.;Skoug,J.W.;Nesbitt,R.R.,Eds.Springer International Publishing:Cham,2020;pp 343-372。 17.Thakur,G.;Hebbi,V.;Rathore,A.S.,Near Infrared Spectroscopy as a PAT tool for monitoring and control of protein and excipient concentration in ultrafiltration of highly concentrated antibody formulations.International Journal of Pharmaceutics 2021,600,120456。 18.Zhang,C.;Springall,J.S.;Wang,X.;Barman,I.,Rapid,quantitative determination of aggregation and particle formation for antibody drug conjugate therapeutics with label-free Raman spectroscopy.Analytica Chimica Acta 2019,1081,138-145。 19.Harz,M.;Stockel,S.;Ciobota,V.;Cialla,D.;Rosch,P.;Popp,J.,Applications of Raman Spectroscopy to Virology and Microbial Analysis.In Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields,Matousek,P.;Morris,M.D.,Eds.Springer Berlin Heidelberg:Berlin,Heidelberg,2010;pp 439-463。 20.Ashton,L.;Lau,K.;Winder,C.L.;Goodacre,R.,Raman spectroscopy:lighting up the future of microbial identification.Future Microbiology 2011,6(9),991-997。 21.Maruthamuthu,M.K.;Raffiee,A.H.;De Oliveira,D.M.;Ardekani,A.M.;Verma,M.S.,Raman spectra-based deep learning:A tool to identify microbial contamination.MicrobiologyOpen 2020,9(11),e1122。 22.Bugno,A.;Saes,D.P.S.;Almodovar,A.A.B.;Dua,K.;Awasthi,R.;Ghisleni,D.D.M.;Hirota,M.T.;de Oliveira,W.A.;de Jesus Andreoli Pinto,T.,Performance Survey and Comparison Between Rapid Sterility Testing Method and Pharmacopoeia Sterility Test.Journal of Pharmaceutical Innovation 2018,13(1),27-35。 23.<USP 1223 Validation of Aternative Microbiological Methods.pdf>.Pharmocopeia,U.S.,Ed。 24.EU5.1.6AlternativeMethodsforControlofMicrobiologicalQuality.Pharmacopeia,E.U.,Ed。 25.Grosso,R.A.et al.Detection of low numbers of bacterial cells in a pharmaceutical drug product using Raman spectroscopy and PLS-DA multivariate analysis.Analyst,2022,147:3593。

[0331] All publications and patents mentioned herein are incorporated herein by reference. Various modifications and variations of the subject matter described will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to these embodiments. Indeed, various modifications for carrying out the invention that will be apparent to those skilled in the art are intended to be within the scope of the following claims.

Claims

1. 1. A method for detecting a microorganism in a sample, comprising: measuring a Raman spectrum of said sample.

2. The method of claim 1 , wherein the sample is exposed to a coherent light source before measuring the Raman spectrum of the sample.

3. The method of claim 1 , wherein the Raman spectrum is measured using a Raman spectrometer.

4. The method of claim 3 , wherein the Raman spectrometer comprises a laser.

5. The method of claim 4 wherein the laser is a multimode diode laser.

6. The method according to claim 4 or 5, wherein the laser wavelength is from 300 nm to 1200 nm.

7. The method of claim 6 , wherein the laser wavelength is 785 nm.

8. 8. The method of any one of claims 1 to 7, wherein the sample is in a bioreactor, a collection tank, a chromatography skid, a chromatography column, an ultrafiltration (UF) skid, a diafiltration (DF) skid, a UF / DF skid, a fill finish tank, or an incubator.

9. The method of any one of claims 1 to 8, wherein the Raman spectrum of the sample is compared with the Raman spectrum of a control sample.

10. A method according to any one of claims 1 to 9, wherein the Raman spectrum of the sample is monitored over time.

11. The method of any one of claims 1 to 10, wherein the monitoring is continuous.

12. The method of any one of claims 1 to 11, wherein the monitoring is non-invasive and / or non-destructive.

13. The method according to any one of claims 1 to 12, wherein the sample has been contacted with an antibody against the microorganism.

14. The method of any one of claims 1 to 13, wherein the sample is contacted with a fluorescent or bioluminescent agent.

15. The method of any one of claims 1 to 14, further comprising detecting fluorescence or bioluminescence of the sample.

16. 16. The method of any one of claims 1 to 15, wherein the microorganism is Cutibacterium acnes, Staphylococcus aureus, Aspergillus brasiliensis, Candida albicans, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, Streptococcus pyogenes, Micrococcus luteus, Burkholderia cepacia, Salmonella typhimurium, or Bacillus cereus.

17. The method according to any one of claims 1 to 16, wherein the measurement of the Raman spectrum is automated.

18. The method of any one of claims 1 to 17, wherein the Raman spectrum is subjected to data analysis.

19. 19. The method of any one of claims 1 to 18, wherein data analysis of the Raman spectra comprises modeling performed using PCA-X, OPLS, KNN, OPLS-DA, PLS-DA, or a combination thereof.

20. The method according to any one of claims 1 to 19, wherein the microorganism is actively growing.

21. 1. A method for continuously monitoring the presence of microorganisms in a sample, the method comprising measuring a Raman spectrum of said sample.

22. 22. The method of claim 21, wherein the Raman spectrum of the sample is monitored over time.

23. 23. The method of claim 21 or 22, wherein the monitoring is non-invasive and / or non-destructive.

24. 1. A method for assaying a test substance, comprising: adding a test substance to a sample containing a microorganism; and measuring a Raman spectrum of the sample.

25. 25. The method of claim 24, wherein the Raman spectrum is measured using a Raman spectrometer.

26. 26. The method of claim 25, wherein the Raman spectrometer comprises a laser.

27. 1. A system for detecting microorganisms in a sample, the system comprising means for measuring a Raman spectrum of said sample.

28. 28. The system of claim 27, further comprising a coherent light source.

29. 28. The system of claim 27, wherein the means for measuring the Raman spectrum is a Raman spectrometer.

30. 1. A system for continuously monitoring the presence of microorganisms in a sample, the system comprising means for measuring the Raman spectrum of said sample.

31. 31. The system of claim 30, wherein the Raman spectrum of the sample is monitored over time.

32. 32. The system of claim 30 or 31, wherein the monitoring is non-invasive and / or non-destructive.

33. A system for assaying a test substance, the system comprising means for measuring the Raman spectrum of a sample to which the test substance has been added.

34. 34. The system of claim 33, further comprising a coherent light source.

35. 34. The system of claim 33, wherein the means for measuring the Raman spectrum is a Raman spectrometer.

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