Patent Application
20220298465
The present invention relates to apparatus and methods for monitoring of cell expansion, particularly for estimating cell density during cell expansion in a generally closed bioreactor by analysing volatile organic compounds (VOCs).
Some development of the use of VOCs in cellular technologies has been reported, for example:
Within process analytical technologies (PAT), it is known that downstream VOC emissions from cell cultures can be utilized by soft sensors for online bioprocess monitoring. A few examples of this have been demonstrated by measuring cellular VOCs with various technologies. It is known to use a so called ‘electronic nose’ which is a monitor of chemical reactions/binding to show total VOC profiles of Chinese Hamster Ovary (CHO) cells tracked with relation to growth in a bioreactor1. Biomass and growth rates were predicted from VOC profiles of Escherichia coli batch cultivations2, and VOCs were used to detect VOC changes in animal cell reactor cultures due to microbial and viral contaminations, including E. coli.3 However, one major disadvantage of the electronic nose technologies mentioned above is the lack of structural information to confidently identify chemical species, which would be an important step toward assessing the biological relevance of targeted VOCs in any analysis. In addition, those sensors drift over time and must constantly be recalibrated, regenerated or replaced.
Other reports have noted that changes in VOC content in headspace can be measured from mammalian cells using traditional mass spectrometry, and those changes correlated with single gene expression levels.4 Mass spectrometry techniques provide additional information for compound identification and have trended towards incorporation as online sensors in reaction monitoring5, including bioreactors. Proton transfer reaction-mass spectrometry (PTR-MS) was incorporated into an E. coli bioreactor and VOC profiles correlated to culture growth6.
Despite the above, there remains a void in regard to the chemical species and quantity of VOCs produced by cells in laboratory cell expansion. In addition, the practical problems of monitoring VOCs in-process, such as maintaining sterility if samples are taken, have not been addressed.
The inventors have recognised the above problems and have also realised that it is possible to correlate VOC profiles from bioreactors with cell density over a significant time period of cell expansion, using non-invasive methods. Their findings show that, for example, for both CHO and T cells, which are important cell expression models for use in bioprocess engineering and cellular immunotherapy workflows, respectively, it is possible to estimate cell numbers using VOC profiles, particularly where VOCs are monitored over time, and utilize the estimated cell numbers to control process parameters. The estimated cell numbers, over time, also provide an indication of cell viability, health, and/or nutrient utilization.
Herein, the term Volatile Organic Compounds (VOCs) includes organic compounds which are dissolved or suspended in a solid, liquid or gas (including vapour or droplets suspended in a gas), as well as organic compounds which and classed as semi-volatile (SVOCs).
The disclosure herein, in summary, provides details of how cell emissions of VOCs were measured from Chinese Hamster Ovary (CHO) cell and T cell bioreactor wastes with the goal of non-invasively metabolically profiling the expansion process. Measurements were made, for example, directly from the gas exhaust lines using sorptive elements, in this case polydimethylsiloxane (PDMS)-coated magnetic stir bars, which underwent subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Baseline VOC profiles of the cell cultures were observed from bioreactors filled with only liquid media (i.e. without cells), and unique VOC profiles correlated to cell expansion over the course of 8 days. Partial least squares (PLS) regression models were built to predict cell culture density based on VOC profiles of CHO and T cells (R2=0.671 and R2=0.769, respectively, based on a validation data set). T cell runs resulted in 47 compounds relevant to cell expansion while CHO cell runs resulted in 45 compounds; the 20 most relevant compounds of each cell type were putatively identified. On the final experimental days, sorbent-covered stir bars were placed directly into cell-inoculated media and into media controls. Liquid-based measurements from spent media containing cells could be distinguished from media-only controls, indicating soluble VOCs excreted by the cells during expansion. A PLS discriminate analysis (PLS-DA) was performed, and 96 compounds differed between T cell-inoculated media and media controls with 72 compounds for CHO cells. The 20 most relevant compounds of each cell line were putatively identified. This work demonstrates that VOC-based detectors can be incorporated in bioreactor gas and liquid waste volumes to non-invasively monitor cellular health and to optimize cell expansion conditions in real time with appropriate control systems. For example, by monitoring cell expansion over time based on the intensity of VOC, an indication of cell viability, health, and/or nutrient utilization can be provided.
The invention, according to one aspect, provides a method for monitoring cell density during cell expansion resulting from a cell culture process in a bioreactor comprising the steps of:
The method may further include a step of:
The method many further also include a step of:
In an embodiment said waste materials include bioreactor headspace gases, and/or filtered liquid waste, and said VOCs include gas phase and/or dissolved or suspended VOCs respectively.
In an embodiment, the waste materials are isolated or removed from the bioreactor chamber prior to said determining.
In an embodiment, said isolation is achieved by an isolation filter allowing only the passage of gases out of the chamber and inhibiting the passage of contaminants into the chamber.
In an embodiment, during or after said process, the VOCs are collected from said waste materials prior to said determining.
In an embodiment, said collecting includes exposing the waste materials to a collective element, such as chemical adsorption or absorption element, and said determining step includes subjecting the collected chemicals to a detector element, for example mass spectrometry (MS) or proton transfer reaction MS, to provide said intensity and profile of VOCs.
In an embodiment, said collecting and said determining are conducted continually, periodically or intermittently.
In an embodiment, said estimating includes assessing the change, and/or rate of change of the VOC concentration/profile.
In an embodiment, said cells are CHO or T cells and the estimation of cell density includes the measurement of the concentration of one or more of alkanes, alkenes, alkynes, carbonyls, esters, alcohols, arenes, acids, amides, amines, carbohydrates, steroids, proteins, nucleic acids and oximes.
In an embodiment, said measurement includes the measurement of the increase in concentration of VOCs, for example, docosane and/or other alkanes.
In an embodiment, a) where said cells are CHO cells, then the measurement includes the measurement of the decrease in concentration of VOCs or b) where said cells are T cells, then the measurement includes the measurement of the decrease in concentration of VOCs, for example, benzaldehyde and/or other aldehydes.
In an embodiment, the ratio of VOCs, for example the ratio of measured alkanes, alkenes, alkynes, carbonyls, esters, alcohols, arenes, acids, amides, amines, carbohydrates, steroids, proteins, nucleic acids and oximes, is used to determine cell density/concentration.
In an embodiment, e) control of at least one process parameter related to the cell culture process includes altering or enhancing cell culture parameters and/or cell culture fluid inputs.
In an embodiment, e) control of at least one process parameter related to the cell culture process includes adjusting chemical and biophysical parameters to further increase expansion, inform harvesting decisions, and control the chemical environment through culture media changes.
The invention, according to a further aspect, provides a cell culture system arranged for monitoring cell density during cell expansion resulting from a cell culture process; the system comprising:
The controller may be further configured to estimate the density or population of cells in the bioreactor based on the determined the intensities of VOCs sensed or collected and the specific combination of the specific chemical species.
The controller may be further configured to provide an indication of cell viability, health, and/or nutrient utilization based upon the estimated density or population of cells over time.
The system may further comprise:
In an embodiment, said at least one waste materials volume includes: a bioreactor headspace for head space waste gases, a waste gas outlet, an area in the chamber where waste fluids collect, a fluid waste collection line or vessel, a fluid circulation line, and/or a solid waste collection line or vessel.
In an embodiment, said one or more VOC collectors include a collection element such as a sorptive element at least partially within the waste materials volume.
In an embodiment, the system further includes an isolation filter allowing only the passage of gases out of the chamber and inhibiting the passage of contaminants into the chamber, and wherein said waste material volume is downstream of said filter thereby isolating the volume from the chamber.
In an embodiment, means for determining the intensity of VOCs collected and their chemical species is a chemical detector, for example mass spectrometry (MS) or proton transfer reaction MS.
In an embodiment, means to control at least one process parameter related to the cell culture process based on the estimation is said controller, the controller being adapted to alter the cell culture parameters in response to the determination of the intensity of VOCs collected and their chemical species and an estimated density or population of cells in the bioreactor based on the determined intensity of VOCs.
In an embodiment, the controller is adapted to adjust chemical and biophysical parameters to further increase expansion, inform harvesting decisions, and control the chemical environment through culture media changes.
The invention extends to any combination of features disclosed herein, whether or not such a combination is mentioned explicitly herein. Further, where two or more features are mentioned in combination, it is intended that such features may be claimed separately without extending the scope of the invention.
The invention can be put into effect in numerous ways, illustrative embodiments of which are described below with reference to the drawings, wherein:
b,c,d and e show the bioreactor of
The invention, together with its objects and the advantages thereof, may be understood better by reference to the following description taken in conjunction with the accompanying drawings, in which, like reference numerals identify like elements in the Figures.
Primary T cells were isolated from buffy coats (sourced from Canadian Blood Services) from 2 donors using a Ficoll density gradient and cultured in T flasks for 6 days prior to inoculation in a Xuri Cell Expansion System (CES, GE Healthcare) at ˜7×105 cells/mL in 1 L of T cell culture medium. T cell culture medium was Xuri Expansion Medium (GE Healthcare) with 1% penicillin-streptomycin (Hyclone), 5% human AB serum (GemCell), and 350 IU/mL Xuri IL-2. CHO-M cells (courtesy of GE Healthcare, Uppsala, Sweden) were cultured in T flasks in ActiPro (Hyclone) medium with 1% penicillin-streptomycin and 2 mM L-glutamine (Hyclone). CHO cells were inoculated in a Xuri CES at ˜2×105 cells/mL in 1 L.
Four 2 L Xuri Cellbags (working volume of 1 L each) with dissolved oxygen (DO) and pH sensors were connected to Xuri CESs. The 2 L Cellbag was inflated with compressed air and 5% CO2 and then left overnight with 200 mL culture medium to equilibrate the DO/pH sensors. Temperature was set to 37° C. and the platform set to rock at 10 rocks per minute (rpm) at a 6° angle. For two minutes in each hour, the platform rocked at 2 rpm at a 2° angle. Perfusion was initiated using a step-wise protocol based on a combination of lactate measurements as well as cell density. Below 2×106 cells/mL, no perfusion was initiated. Above 2×106 cells/mL, medium was perfused at 0.5 L/day at VCD between 2×106-10×106 cells/mL, at 0.75 L/day for VCD between 10×106-15×106 cells/mL, and at 1 L/day for VCD greater than 15×106 cells/mL. A 1 L/day perfusion was initiated regardless of the VCD in the event of a lactate concentration exceeding 20 mM.
Bioreactor air exhaust was directed via PTFE tubing through the lid of a capped borosilicate jar. Each bioreactor employed was connected with a single jar and the same jar was used throughout the course of the entire experiment. Each jar contained four sterile and pre-conditioned HSSE stir bars (“Twisters®”, Part 011222-001-00, Gerstel US, Linthicum Heights, Md.), held in place to the side of the jar by magnets, providing four technical replicates per sample. The commercially available HSSE bars were 10 mm in length and contained a 0.5 mm thickness of polydimethylsulfide (PDMS) sorbent. Twisters® were left to extract cell culture VOCs in 24 h increments. After this period, the lids were removed from the jars, the four Twisters® were collected and replaced with four fresh HSSE bars, and the lid was screwed back onto the jar.
A final time point measurement to examine VOCs dissolved in the liquid media was made using Twisters® in a stir bar sorptive extraction (SBSE) immersion technique. This was not performed until the end of the experiment to reduce the risk of cell culture contamination. During the final 24 h of the experiment, four sterilized Twisters® (soaked in 70% ethanol for 10 min) were dropped directly into each cell culture via a port on the CellBag bioreactor. Once extraction was complete (24 h), the bioreactor bags were sliced open and the Twisters® were collected. The experiment ended at this point and cells were destroyed. For media only controls, additional Twisters® were placed directly into 20 mL of cell-free media of each type for 24 h and incubated at the same temperature as the cultures.
Twice a day, an aliquot (5-10 mL) from the bioreactors was collected for measurements of culture attributes/metabolites: viable cell density (VCD), % viability, glutamine, glutamate, glucose, lactate, ammonium, sodium, potassium, calcium, pH and pO2. VCD and viability were measured on a Nucleocounter NC-200 (Chemometec, Allerod, Denmark). Metabolite measurements were conducted on a BioProfile FLEX 2 Analyzer (Nova Biomedical, Waltham, Mass.).
Twister®-GC-MS Analysis
There were 2 biological replicates for T cells and 2 technical replicates for CHO cells, with 4 technical replicates of each per time point. All Twisters® were pre-conditioned prior to use, according to manufacturer specifications.
As soon as Twisters® were extracted from the cell culture reactors, they were placed into 2 mL borosilicate vials and an aliquot of the first internal standard (1 μL of a 1 ppm naphthalene-D8 in ethanol solution) was pipetted into each vial. Twisters® were kept frozen until analysis. Just prior to analysis, they were transferred into thermal desorption tubes alongside an aliquot of the second internal standard (1 μL of a 0.1 mL/L decane-D22 in ethanol).
Individual Twisters® were thermally desorbed using a thermal desorption unit (TDU, Gerstel US) and cooled injection system (CIS, Gerstel US). The TDU was initially set to 30° C. for 0.5 min and heated at 60° C./min until reaching 300° C. and held for 3 min. A flow of helium led desorbed analytes into the CIS, which was held at −80° C. After desorption, the CIS heated at 12° C./s to 300° C. and was held for 3 min. This process splitlessly injected analytes onto the head of the GC column.
Chromatography occurred on an Agilent 7890A GC (Agilent Technologies Inc., Santa Clara, Calif.) equipped with a DB-5 ms column (30 m×250 μm×0.25 μm, Agilent Technologies Inc.). The column was initially at 35° C. for 3 min, then heated at 2° C./min to 200° C., then heated at 30° C./min to 300° C. and held for 5 min. Total runtime was 93.8 min. The GC was operated in constant flow mode (1.5 mL/min of helium). Analytes eluted into a 5975C single quadrupole mass spectrometer (MS, Agilent Technologies Inc.). The MS scanned from 33 to 300 m/z. Its source and quad were set to 230° C. and 150° C., respectively.
A bake out of the TDU-CIS-GC-MS system was conducted every ˜20 injections. After every 30-40 GC-MS injections, a standard mixture of C8-C24 alkanes was analysed to serve as an external 20 control of the instrument and also to calculate Kovats retention indices of compounds.
GC-MS data files were deconvoluted and aligned using the recursive feature extraction on Profinder (Version B.08.00, Agilent Technologies Inc.). Peak areas were normalized to the first internal standard. Features with siloxane base peaks (73, 147, 207, 221 and 281 m/z) were removed. Statistical analyses were performed using GeneSpring (Version B.14.9, Agilent Technologies Inc.) and PLS Toolbox (Version 8.6, Eigenvector Research Inc., Manson, Wash.). A p-value of p<0.05 was used throughout for significance. Putative peak identification was possible through spectral matching with the NIST 14 mass spec database along with comparison of calculated Kovats Retention Index comparisons to reported literature values.
To model changes in VOC profiles related to cell growth, HSSE data from both CHO cell reactors were pooled together and VOC data from both T cell reactors were pooled together, and data were autoscaled. Within each of these two groups, the data were randomly separated: 67% for a calibration training set and 33% for a validation set. Partial least squares regression (PLS) was applied to correlate live cell densities (the Y space) to the VOC profiles (the X space) using PLS_Toolbox software (Eigenvector Research Inc., Manson, Wash.). Cross-validation was performed using the venetian blinds technique, where the calibration data were split into 10 random splits and one sample per split was used to cross-validate the model. To cluster compounds of similar changes in intensity, agglomerative hierarchical clustering was applied using the shortest distance algorithm in MATLAB R2017a software (MathWorks, Natick, Mass.).
SBSE data were divided into the two cell types and their respective controls. A PLS-discriminate analysis (PLS-DA) was performed on each cell type to categorically distinguish media controls from cell samples.
At the time of media inoculation, the concentrations of CHO cells were 2.2×105 and 2.6×105 cells/mL per reactor respectively, and T cells were 7.0×105 and 8.0×105 cells/mL (
Measured metabolites are also provided in
Principal components analysis (PCA) was applied to all HSSE samples (
In addition to separating from controls, there was a trend for cell types to separate (
Prior to any statistical analysis, including PCA, samples were normalized to the internal standard. This practice would account for any potential signal drift caused by the GC-MS instrument. Further, visualization of the internal standards results do not suggest an instrument drift occurred (data not shown), confirming that changes in the VOC profile must have related to changes in the bioreactor.
To correlate cell growth to VOC profiles, two PLS regression models were built, one for CHO cells and one for T cells. Within each cell type, 67% of data were used to train and calibrate the PLS model, which was then applied to the remaining 33% as a blinded validation set. Models showed a correlation between the live cell density and the VOC profiles collected using the HSSE-GC-MS extraction technique (
In a PLS analysis, variable importance in projection (VIP) scores are generated for each variable (in this case, a chemical VOC of interest). Variables with a VIP score greater than 1 are typically considered relevant to the regression. T cells had 47 compounds with a VIP>1 , and CHO cells had 45 compounds; 26 compounds overlapped between the two cell lines.
Putative identifications were made on the 20 compounds with the highest VIP score for the T cell model and the 20 compounds with the highest VIP score for the CHO model (Table 2). 27.0% of these compounds were classified as a type of alkane, while 15.4% were esters, 7.7% alcohols, 7.7% oximes, and 23.0% others with 19.2% unknown.
By using HSSE-GC-MS, we believe we are the first group to report the identities of VOCs emitted by CHO and T cells in a bioreactor during cell expansion. Without other studies to offer comparison, we compare these results to other cell culture experiments and find that the types of VOCs identified in this work are in general agreement. 2-ethyl-1-hexanol was found relevant to viral infections of human laryngeal cancer cells.16 Benzaldehyde has been observed in emissions of human fibroblasts (hFB). 17 Esters have been observed in cultures of human B-lymphoblastoid cells. 18 Alkanes and alcohols have been observed in epithelial cell cultures. 15 While known background compounds were not included in statistical analyses, such as siloxanes from the PDMS sorbent and GC column bleed, phthalates might be artefacts from the plastics within the bioreactor system.
Some compounds increased in intensity with cell expansion while others decreased. To group compounds by patterns of change, hierarchical clustering was applied to the top 20 CHO and 20 T cell compounds from Table 2. Each dendrogram was divided in such a way to yield four clusters of VOCs. Each cluster was plotted to demonstrate the compounds' intensities over the course of the 8 d of cell expansion (
The majority of these most relevant VOCs decreased during cell expansion (Cluster 4 compounds,
SBSE measurements made directly in bioreactor bags isolated more cellular VOCs from media controls than HSSE measurements of bioreactor gas exhaust. A PCA of these liquid-phase extractions (
Two PLS-DA analyses were performed that distinguished liquid media controls from respective cell lines. Similar to PLS regression, each variable (in this case, chemical VOC compound) was assigned a VIP score. CHO cells had 72 compounds with a VIP score >1 and T cells had 96 compounds, with 43 overlapping between cell lines. T cells had 16 compounds with VIP scores >1 in both downstream VOC emission measurements (HSSE) and cell-inoculated liquid measurements (SBSE); there were 9 such compounds for CHO cells.
The 20 compounds with the highest VIP scores for each cell types were putatively identified (Table 3). Not all these compounds were present in liquid media controls. Compared to HSSE, SBSE extracted more compounds of higher molecular weights. Many contain aromatic rings (toluenes, phenols, benzoic acids, benaldehydes, acetophenones, etc.). One compound, unknown 10, appears in both Table 2 and Table 3, having importance only in CHO cells in both HSSE and SBSE measurements.
Some compounds appear related to the mevalonate pathway. Important to cell membrane function and steroid synthesis, cholesterol was putatively identified in both CHO and T cell bioreactors. A derivative of citronellol was found in CHO cells, which may be a hydrogenated product of geraniol, a compound involved in cholesterol synthesis pathways. 19 P-benzoquinone could be attributed to exposure to benzene derivatives or as a breakdown product of ubiquinone. Naphthols such as 1-amino-2-naphthalenol may derive from biomarkers related to exposure to polycyclic aromatic hydrocarbons, such as plasticizers. 20 Heretocyclic compounds such as quinazolines, quinolinones and pyrazoles may have resulted from other steroids.
Similar to gas exhaust, chemical sensors could be attached to the media waste lines of the bioreactors to monitor target compounds related to cellular health or to perform untargeted analysis to warn users when the waste stream has deviated from a “normal” state. This could help optimize media perfusion rates by monitoring waste and nutrient concentrations within the bioreactor.
We observed a shift in the specific VOC profile of bioreactor gas exhaust as cell cultures expanded over the course of 8 days. These profiles were used to create PLS regression models that could predict cell culture densities. The volatile compounds most relevant to cell culture expansion for CHO and T cells were putatively identified and discussed. Additionally, measurements of VOCs were made directly in cell-inoculated media during the final day of the experiment. Cell-inoculated media samples were rich in VOCs not present in liquid media controls (no cells present). A PLS-DA analysis revealed the volatile compounds most relevant to the cell cultures and were putatively identified and discussed. Thus, it has been demonstrated that is possible to use VOC-based detection methods on either gas or liquid waste lines of bioreactors to monitor cell health.
Further, by determining a population size and/or density of cells by VOC-based detection, at least one process parameter related to the cell culture process may be controlled. For example, a controller connected to a bioreactor system may be adapted to alter the cell culture parameters in response to the determination of the intensity of VOCs collected and their chemical species and an estimated density or population of cells in the bioreactor based on the determined the intensity of VOCs.
The controller, thus, may adjust chemical and biophysical parameters to further increase expansion, inform harvesting decisions, and control the chemical environment through culture media changes.
Although one embodiment of a cell culture system has been described and illustrated, it will be apparent to the skilled addressee that additions, omissions and modifications are possible to those embodiments without departing from the scope of the invention claimed. For example, the invention has been demonstrated using CHO cell and T cells, however it would be apparent to the skilled addressee that the invention could be employed with equal effect to assess populations of other cells such as, but not exclusively, for therapeutic applications: other lymphocytes such as so-call natural killer cells (NK cells), tumour infiltrating lymphocyte cells (TIL cells); different sub-groups of T cell such as regulatory T cell (Treg cells); antigen-presenting cells such as dendritic cells (D cells); modified cells such as chimeric antigen receptor modified T cells (CAR-T cells), gamma-delta T cells (γδ T cells); and for research, cell populations of other cells such as Vero cells.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/065927 | 6/9/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16441883 | Jun 2019 | US |
| Child | 17617274 | US |
