The rapid qPCR assay described in this tech note is a cell-based technology developed by SK pharmteco to monitor the emergence of rcAAVs. The company has also developed technologies to mitigate the risk of rcAAV formation. Together, these technologies can help manufacturers of gene therapies sustain high-throughput operations, improve quality control, and enhance the safety and efficacy of their products.

The production of recombinant AAVs involves the co-transfection of three plasmids into HEK293 cells: the rep-cap plasmid, the adenovirus helper plasmid, and the AAV genome plasmid. The rep-cap plasmid carries the AAV replication (rep) and capsid (cap) genes essential for AAV genome replication and capsid formation. The adenovirus helper plasmid provides essential helper functions for AAV replication and packaging, whereas the AAV genome plasmid contains the therapeutic gene flanked by inverted terminal repeats (ITRs). The triple-transfection method is efficient and scalable, enabling the production of various AAV serotypes by altering the cap gene within the rep-cap plasmid.

However, during recombinant AAV production, nonhomologous recombination between the AAV vector and packaging DNA can lead to the formation of rcAAVs. These replication-competent vectors, if not managed properly, can replicate in the presence of a helper virus, potentially leading to unintended infection and replication within host cells. The presence of rcAAVs in clinical-grade AAV vector preparations is not just a concern, but a significant and pressing issue, as their behavior in the host post-administration, especially in the presence of natural helper viruses, is not fully understood.

Advanced detection methods

To address the challenge of rcAAVs, stringent quality control measures and advanced diagnostic techniques are required. Traditional methods for detecting rcAAVs, such as PCR-based assays, are effective but labor intensive and unsuitable for high-throughput commercial manufacturing. Recent advancements include the use of single-molecule, real-time sequencing and AAV genome population sequencing to detect and characterize rcAAVs. These technologies offer high sensitivity and specificity, and they are capable of identifying diverse recombination events leading to rcAAV formation.

At SK pharmteco, a cell-based qPCR assay has been developed to monitor the emergence of rcAAVs. This assay uses rep2-specific primers/probes, followed by confirmation with cap gene–specific primers/probes, to detect rcAAV events at a limit of detection of 10 infectious units. The qPCR assay is integrated into the manufacturing process, enabling timely detection and mitigation of rcAAV formation to ensure patient safety.

The application of the cell-based qPCR assay at SK pharmteco has proven robust and reliable. Transient transfection, involving multiple steps—such as cell expansion, plasmid DNA-transfection reagent complex formation, and bioreactor transfer—leads to the generation of viral particles (Figure 1).

The qPCR assay monitors for rcAAV emergence through continuous rounds of propagation of harvest lysates in the presence of adenovirus (Figure 2). This method amplifies AAV genomes containing ITR–rep-cap–ITR sequences, increasing progeny virus through subsequent rounds of infection. Extensive testing validated the assay’s specificity and sensitivity. Specificity acceptance criteria ensured accurate identification of target sequences, with positive amplification detected only in positive controls and test particles (Table 1).

The assay’s ability to detect the lowest concentration of rAAV2 confirmed the detection limit, ensuring high sensitivity. The linearity of the assay was evaluated through quantitative PCR standard curves, verifying accuracy across a range of input concentrations.

A bridging study was also conducted to evaluate the assay’s applicability to other AAV serotypes. The study confirmed that the assay design suits various AAV serotypes, demonstrating its broad utility in viral gene therapy applications. Primers and probes for AAV2 were validated, ensuring reliable detection and quantification of rcAAV2. Compatibility with different adenovirus preparations further enhanced the assay’s flexibility and practicality.

Importance of results for gene therapy production

The ability to reliably detect and mitigate rcAAV formation is crucial for the safety and efficacy of gene therapy products. The presence of rcAAVs poses significant risks, including potential unintended replication and infection within the host. Advanced detection technologies, such as the cell-based qPCR assay, provide a robust solution to monitor and control rcAAV emergence during manufacturing. Ensuring the absence of rcAAVs in clinical-grade AAV vector preparations enhances the safety profile of gene therapy vectors, reducing the risk of adverse events in patients.

The scalability and high-throughput capability of the qPCR assay make it suitable for commercial manufacturing settings. This ensures that quality control measures can be consistently applied across large production volumes, maintaining the integrity and safety of gene therapy products.

In addition to advanced detection methods, SK pharmteco has implemented several strategies to mitigate the risk of rcAAV formation. Promoter rearrangement is one strategy, where the P5 promoter is positioned 3′ of the cap gene in the vector design. This selective promoter activity prevents unintended gene expression that could contribute to rcAAV formation. Incorporation of introns to exceed AAV’s packaging limit is another strategy, ensuring that intact contigs cannot be packaged, thus reducing rcAAV emergence.

Observational data revealed that configuring test article constructs with the P5 promoter positioned upstream of essential replication and capsid genes led to a higher risk of rcAAV formation. Conversely, positioning the P5 promoter 3′ of these genes significantly reduced this risk, supporting the influence of promoter placement on vector safety. These mitigation strategies, combined with advanced detection methods, provide a comprehensive approach to ensuring the safety and efficacy of rAAV-based therapies.

Future directions

The ongoing development and refinement of the assay design focuses on enhancing sensitivity and accuracy. Integrating adenovirus 5 (Ad5) earlier in the protocol and optimizing freeze/thaw cycles are potential adjustments to improve assay performance. Shifting from qPCR to droplet digital PCR for endpoint analysis could offer superior precision and sensitivity, which would be particularly useful in detecting low levels of target DNA.

Future studies will involve pilot testing these adjustments, systematic analysis of freeze/thaw cycle effects, and controlled experiments to confirm the benefits of early Ad5 addition. Further research will explore alternative promoter and gene configurations to robustly prevent rcAAV formation without compromising vector efficacy. These efforts aim to enhance the capability to safely and effectively utilize rAAV vectors in clinical settings.

At SK pharmteco, Oxana M. Tsygankova, PhD, serves as senior scientist; Lana Sweet, director of virology, cell and gene therapy; Dana Cipriano, global head of testing and analytics; and Brian Tomkowicz, PhD, head of viral vector R&D.

The post Adapting a Replication-Competent AAV Assay for Commercial Manufacturing appeared first on GEN - Genetic Engineering and Biotechnology News.

Perhaps the greatest challenge facing cell therapies for this purpose lies in the immunosuppressive mechanisms found in the tumor microenvironment. This is harsh biological territory, with high interstitial pressure, reduced oxygen tension, and a barrage of immunosuppressive proteins. Studies show that therapeutic cells exposed to this environment suffer from depletion, exhaustion, and mitochondrial dysfunction.

Thus, there is a critical need for manufacturing processes to generate more potent cell populations, both by producing higher proportions of antigen-targeted cells (for example, chimeric antigen receptor T cells, or CAR T cells) and by boosting the cytotoxic potential of those cells.

New evidence indicates that cell therapies can be “rewired” metabolically to overcome these challenges, leading to treatments that could be far more effective against a range of solid tumors. The idea is simple: instead of growing therapeutic cells in conventional culture conditions designed to keep cells as happy as possible, why not acclimate them under conditions mimicking that of the tumor microenvironment they will have to face in vivo?

The cells that survive and expand in vitro under these harsh conditions should be far more likely to remain effective when they reach the tumor microenvironment. Results are already showing that metabolic rewiring can create higher-yielding, effective cell therapies.

Feasible approach

This approach is feasible for any scientist developing cell therapies. For the work described below, teams at Xcell Biosciences and Labcorp used the AVATAR system to compare the performance and effectiveness of CAR T cells grown in conventional culture conditions versus conditions more closely mirroring the tumor microenvironment.

Cells were prepared according to established cell therapy manufacturing protocols and cultured under a range of O2 and pressure levels. We first assessed three different culture environments: standard culture conditions with no change to oxygen or pressure in a conventional CO2 incubator (20.5% O2 with 0 pounds per square inch (PSI)); the AVATAR incubator set to pressure and oxygen levels replicating the arterial vasculature system at 15% O2 with pressure at 2 PSI; and the AVATAR incubator at 15% O2 and pressure at 5 PSI. All incubation conditions were maintained at 5% CO2 and 37°C.

CD3 T cells were thawed alongside soluble anti-CD3 and cultured for two days. All three cell populations were then transduced with a lentivirus designed for CD19 CAR expression and returned to their incubator conditions to grow for 10 more days. CD19 CAR expression was evaluated with flow cytometry, and cytotoxic activity was assessed with a targeted killing assay.

These in vitro experiments demonstrated that culturing with adjusted O2 and pressure levels had no detrimental effect on the therapeutic cells, and these cells actually outperformed cells grown in standard incubator conditions (Figure 1). For example, we found that both cell populations grown with modified oxygen and pressure settings had higher proportions of CAR T cells following lentiviral transduction compared to the conventional incubator population. Cells transduced in the conventional CO2 incubator conditions yielded 10% to 20% CD19 CAR T cells, while those transduced in high-pressure conditions yielded as much as 40% of the desired therapeutic cells.

By the end of the expansion process, cell yield was more than twice as high in the two groups expanded at 15% O2 and at 2 or 5 PSI as compared to the standard incubator cells.
Potency tested

The potency of CD19 CAR T cells manufactured in the three culture environments was next tested in vitro with a targeted NALM6 cytotoxicity assay. For this work, we co-cultured T cells with NALM6, CD19-expressing target cells, for 48 hours, and measured lysis rates by flow cytometry. As expected, CD19 CAR–specific killing was exhibited in a dose-dependent manner, and cells grown in the AVATAR system showed no impairments to their cytotoxic function.

Cytotoxicity was confirmed in a mouse model of B-cell acute lymphoblastic leukemia. CD19 CAR T cells were administered to mice that had been inoculated with NALM6 cells intravenously. For five weeks, we regularly measured tumor growth with bioluminescence imaging, and we collected blood samples to identify circulating T cells and assess their phenotype using flow cytometry.

Results showed that mice treated with the two AVATAR cell populations had good tumor control outcomes, confirming that the modified culture conditions do not impair cancer-fighting ability in vivo (Figure 2). We also examined blood samples for T cells using Labcorp’s Custom Expanded Persistence T Memory Panel. The animals exhibited persistence of the therapeutic cells in relevant organs, and the CAR T cells maintained their phenotype with strong central memory and effector memory populations.

Finally, we performed experiments on CD19 CAR T cells grown in conditions of even more acute hypoxia, with conditions of 5%, 10%, and 15% oxygen, while maintaining 5 PSI, and compared them to cells grown in the conventional CO2 incubator. We again evaluated the cells’ potency in vitro with a targeted NALM6 cytotoxic assay, but importantly, tested the ability of CD19 CAR T cells to kill for extended time periods at low effector-to-target cell ratios (Figure 3).

The improved cytotoxic function of CD19 CAR T cells grown under lower oxygen conditions is currently being confirmed in further in vivo experiments. While this work is being performed with a mouse model for hematological cancer, follow-up studies using this same approach for solid tumors are already underway.

All of this work was completed with well-established protocols for cell therapy development, with no special techniques required for the cells expanded in the AVATAR system. In the future, a more automated workflow will be enabled with the upcoming AVATAR Foundry system, which has been designed specifically for the manufacture of cell therapies. This would allow clinical research teams to incorporate hyperbaric pressure and oxygen control into the manufacturing process more easily, accelerating the process of discovering the optimal environmental conditions to enhance the potency and yield of each therapy candidate.

The GMP-compliant AVATAR Foundry system is currently available to scientists through a beta access program. To see more of this data, view a poster (“Metabolic reprogramming enhances expansion and potency of CAR-T cells”) that Xcell Biosciences and Labcorp scientists presented at the American Association for Cancer Research Annual Meeting in 2024.

James Lim, PhD, is a co-founder and the CSO of Xcell Biosciences. Scott Wise, MS, is executive director of preclinical oncology at Labcorp.

The post Unlocking Cell Therapy’s Full Potential by Rewiring T-Cell Metabolism appeared first on GEN - Genetic Engineering and Biotechnology News.

One area for improvement is the transfection process used in the upstream manufacturing of AAV gene therapies. The TransIT-VirusGEN^® transfection solution includes both polyamine-containing polymers and lipids that help overcome barriers during transfection, enabling high transfection efficiency and low cellular toxicity. The polymer facilitates nucleic acid condensation, binding, and uptake by the cells, while the lipid promotes endosomal escape.

The RevIT AAV Enhancer can be used in conjunction with both the VirusGEN solution and conventional polymeric transfection reagents to produce 2–4X higher genome titers in suspension HEK 293 cells. The enhancer is simple to use, easily integrates into existing workflows, and produces high-quality titers across a range of AAV serotypes and cell growth media. When combined with the TransIT-VirusGEN solution, the enhancer enables use of lower amounts of plasmid DNA (pDNA), which represents a key cost-saving opportunity.

Improved genome titers across multiple AAV serotypes

The efficacy of RevIT AAV Enhancer was assessed across multiple serotypes (AAV2, AAV5, AAV8, and AAV9) in 293-VP 2.0 cells (Thermo Fisher) using TransIT-VirusGEN and the single-component polymeric transfection reagents (Figure 1). In all serotypes, use of the RevIT AAV Enhancer in conjunction with TransIT-VirusGEN increased genome titers 1.7- to 2.4-fold compared to the TransIT-VirusGEN control. The RevIT AAV Enhancer plus TransIT-VirusGEN condition also delivered up to 6-fold higher genome titers and 2.9-fold higher percent full capsids. RevIT AAV Enhancer increased genome titers 1.7- to 2.2-fold with other transfection reagents compared to their respective controls, demonstrating broad applicability.

The enhancer was also tested in AAV2, AAV5, AAV8, and AAV9 serotypes using 293-VP 2.0 cells grown in either Viral Production Medium (Thermo Fisher) or BalanCD HEK 293 Medium (Irvine Scientific). There was little to no difference in performance across these growth media formulations (Figure 2). Together, these data demonstrate broad serotype and growth medium compatibility when using RevIT AAV Enhancer in conjunction with TransIT-VirusGEN and single-component polymeric transfection reagents.

Cost savings achieved with lower pDNA doses

Protocols for commonly used single-component polymeric transfection reagents recommend pDNA doses of 1 μg/106 cells (for example, 3 μg/mL at a density of 3 × 106 cells/mL) for triple-transfection-mediated AAV production in suspension cells. In contrast, the TransIT-VirusGEN protocol recommends a pDNA dose of 2 μg/mL of cell culture regardless of cell density, which represents a 33% decrease in pDNA usage. Scaling pDNA dosage by cell density does not improve viral titers, even when RevIT AAV Enhancer is employed, suggesting that maintaining a pDNA dose of 2 μg/mL cell culture with TransIT-VirusGEN can reduce the usage of valuable pDNA and save on manufacturing costs.

Lower amounts of pDNA with RevIT AAV Enhancer were tested, demonstrating that the enhancer allowed for a decrease in pDNA doses to as low as 0.75 μg/mL in some serotypes while still maintaining high genome titers (Figure 3). This allows for up to a 75% decrease in pDNA usage compared to a traditional DNA dose method of 3 μg/mL pDNA for a density of 3 × 106 cells/mL. Decreasing pDNA doses also led to a higher percentage of full capsids, demonstrating that drastic cost savings and higher quality AAV can be achieved using lower pDNA doses in conjunction with RevIT AAV Enhancer.

A proven strategy to increase titers and decrease costs

Use of the RevIT AAV Enhancer substantially increases AAV genome titers across multiple serotypes and transfection platforms, including the TransIT-VirusGEN transfection reagent and polymeric transfection reagents. Simple optimization allows for fast and easy integration of RevIT AAV Enhancer into existing AAV manufacturing workflows to increase titers by 2–4X. The enhancer also enables reductions in the amount of pDNA required during the transfection process, leading to considerable cost savings in AAV-based gene therapy manufacturing.

Download our white paper on use of the RevIT AAV Enhancer for additional studies and
details on materials and methods:(https://www.mirusbio.com/content-download-revit-aav-enhancer-white-paper/).

Becky Reese, PhD, is senior scientist, Jennifer Swanson is R&D associate scientist III, Austin Storck is associate scientist III , and Laura Juckem, PhD, is vice president of research and development at Mirus Bio.

References

1. Au HK, Isalan M, Mielcarek M. Gene therapy advances: A Meta-Analysis of AAV Usage in Clinical Settings. Front. Med. (Lausanne) 2022; 8.

2. Clément N, Grieger JC. Manufacturing of recombinant adeno-associated viral vectors for clinical trials. Mol. Ther. Methods Clin. Dev. 2016; 3: 16002. 

The post Increased Transfection Efficiency Boosts AAV Titers for Gene Therapies by 2–4X appeared first on GEN - Genetic Engineering and Biotechnology News.

The most common in vivo screening assay is the QT interval measured from a surface electrocardiogram, which represents the summation of the action potentials of ventricular cardiomyocytes (Figure 1A). Prolongation of the rate-corrected QT interval beyond 440 ms is associated with an increased risk of the polymorphic ventricular tachycardia (increased heart rate) called torsades de points (TdP), which is characterized by a “twisting of the points” around the isoelectric line (Figure 1B). Although TdP is not itself fatal, it can lead to life-threatening ventricular fibrillation and sudden cardiac death.^4,5

Mutations in 16 different genes have been linked to familial forms of long QT syndrome. However, certain drugs have a propensity to prolong the QT interval and are associated with an increased risk of TdP. Although the incidence of drug-induced QT prolongation and TdP is low, the mortality rate is between 10% and 20%.⁶

Between 1990 and 2006, drug-induced QT prolongation and TdP presented a significant safety challenge, resulting in the withdrawal of several drugs (for example, cisapride) from the market due to their proarrhythmic liability.⁷ It was later discovered that these drugs inhibited the human ether-à-go-go related gene (hERG) potassium channel. This ion channel controls the flow of potassium ions from within ventricular cardiomyocytes into the extracellular space. The channel activates rapidly upon initiation of the action potential (depolarization), which underlies why it is referred to as the rapid delayed rectifier potassium current (IKr). This current is essential for controlling the duration of ventricular action potentials and, therefore, fundamental to controlling the QT interval.

Responding to this challenge in 2005, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) released nonclinical and clinical cardiac risk evaluation guidelines (ICH E14/S7B). As part of the ICH guidelines, in vitro hERG screening during drug discovery and nonclinical drug development is now mandatory for screening small-molecule INDs to detect proarrhythmic activity.⁸

Additionally, a recent update to the guidelines allows for the use of validated GLP in vitro screening and in vivo QTc-negative results to waive the need for a clinical thorough QT (TQT) study.

In vitro GLP-compliant hERG screening

The introduction of in vitro ICH-mandatory GLP-compliant hERG screening during drug discovery and nonclinical drug development has significantly reduced the development of proarrhythmic drugs,9 and so far, it has successfully prevented any further new drug withdrawals from the market due to TdP causation.⁷

The GLP assessment of compounds against hERG is most commonly performed using the conventional whole-cell patch-clamp technique. For this assay, the hERG channel is overexpressed in a recombinant cell expression system, such as Chinese hamster ovary cells. The conventional whole-cell patch-clamp technique involves establishing a seal between the tip of a glass pipette electrode and the membrane of the cell expressing hERG. Once the seal has been obtained, negative pressure and/or electrical current is used to rupture the cell membrane and produce the whole-cell configuration.¹⁰ The glass pipette contains an electrode that is in contact with the intracellular solution of the cell. A ground electrode is located in the bath, which allows the voltage across the cell membrane to be clamped; this allows the operator to control the function of voltage-sensitive ion channels, such as hERG.

Once the whole-cell configuration has been achieved, the FDA’s recommended voltage protocol can be used to elicit hERG current. Initially, the cell is superfused with a vehicle control solution to generate a baseline reading (Figure 2A). Once the hERG current has stabilized, the cell can be superfused with a test solution to determine whether it inhibits the hERG current. Figure 2B shows the effect of 1 and 10 µM ondansetron (an antiemetic agent) on the hERG current. There was a rapid and concentration-dependent inhibition of hERG. A supramaximal concentration of a positive control compound, E-4031, was used to fully block hERG current at the end of each experiment. These assays support the preclinical stages of drug development. A conventional whole-cell patch-clamp technique measuring biological electrophysiology (ion currents) can be performed as a “gold standard” following the FDA’s recommended voltage protocols to study drug-cardiac ion channel interactions before first-in-human studies.⁹

Vital importance of high-quality data

The ICH S7B guidelines stipulate that in vitro hERG assessments should be performed to GLP compliance. In addition, the recently released ICH E14/S7B 2022 Q&A provides recommendations on the experimental methods that should be employed, the quality control parameters for analyzing the data, and the preferred format for reporting the data.⁹ In line with the best practice recommendations, Metrion Biosciences developed and validated its own GLP hERG assay.

Case study: GLP-compliant hERG assay validation

The study, conducted to GLP compliance, used the conventional whole-cell patch-clamp technique to establish potency data and safety margins for the FDA’s three recommended positive control drugs: ondansetron, moxifloxacin (antibiotic), and dofetilide (antiarrhythmic), which each have well-defined torsadogenic (TdP causing) risk profiles.11 The FDA’s QC parameters were monitored during the experiment and during offline analysis to ensure high data quality.

The relative potency of a drug to inhibit the hERG current is expressed as the half-maximal inhibition concentration (IC50), which is the drug concentration at which 50% of the hERG current is inhibited.¹² The IC50 values and associated safety margins generated for the three compounds in the study were consistent (within a twofold difference) with the values published in the E14/S7B 2022 training materials (Figure 3). In addition, the IC50 value generated for ondansetron (1.55 µM 95% CI: 1.25 to 1.93) was consistent with that generated from a previous non-GLP study performed at the same test facility (1.72 µM 95% CI: 1.51 to 1.95), which demonstrates the consistency of this established assay.

The GLP hERG assay was successfully validated by Metrion Biosciences using ondansetron, moxifloxacin, and dofetilide to U.K. GLP compliance and according to the experimental recommendations outlined in the ICH E14/S7B Q&A guidance (Figure 3). The potency of each standard was in alignment with the ICH E14/S7B training material values. This validation ensures that each IND assessed generates high-quality data ready for incorporation into small-molecule IND applications.

Thorough QT waivers

The recently released ICH E14/S7B 2022 Q&As provide an opportunity to combine GLP-compliant hERG data with in vivo corrected QT (QTc) data into an integrated risk assessment pathway, which may negate the need to perform a clinical TQT study.

Compounds displaying a negative result for both the in vivo QTc and in vitro GLP-compliant hERG studies with respect to their respective safety margins over the expected free clinical exposure are considered to have a “double negative” profile. These data can be used to generate a TQT waiver application that may, if accepted by the regulatory authorities, negate the need to run a clinical TQT study.

Clinical TQT studies can be overly sensitive but not very specific, resulting in false positives where candidate compounds might not be proarrhythmic in nature.¹³ Furthermore, clinical TQT studies are costly, usually ranging between $2 million and $4 million. While this may represent less than 1% of the overall cost of taking a drug to market, it is still a major expense, especially given that each IND needs to be screened.¹⁴

Obtaining a waiver by achieving a “double negative” result can save time, reduce costs, and conserve resources significantly. For this, high-quality data for full candidate concentration-response curves and a suitable reference standard that matches the FDA’s data are critical. Expertise in this area is essential for expediting such testing and ensuring high-quality data generation and reporting.

Conclusion

It is of significant benefit to drug discovery scientists to identify potentially proarrhythmic drugs at the preclinical stage. The recently released ICH E14/S7B 2022 Q&As recognize the value of preclinically validated in vitro GLP-compliant hERG studies in predicting the liability of potentially proarrhythmic drugs. Importantly, when this assay is used with in vivo QTc data, it can waive the need for costly and time-consuming clinical TQTs—potentially accelerating the delivery of promising new candidate drugs to market and, critically, to patients in need.

Steve Jenkinson, PhD, is vice president of drug discovery and safety assessment at Metrion Biosciences.

References

1. Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat. Rev. Drug Discov. 2019 Jul; 18(7): 495–496. DOI: 10.1038/d41573-019-00074-z. PMID: 31267067. 

2. Harrison RK. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug. Discov. 2016 Dec; 15(12): 817–818. DOI: 10.1038/nrd.2016.184. Epub 2016 Nov 4. PMID: 27811931. 

3. Laverty H, Benson C, Cartwright E, et al. How can we improve our understanding of cardiovascular safety liabilities to develop safer medicines? Br. J. Pharmacol. 2011 Jun; 163(4): 675–693. DOI: 10.1111/j.1476-5381.2011.01255.x. PMID: 21306581; PMCID: PMC3111672. 

4. Mayo Foundation for Medical Education and Research. Long QT syndrome. Accessed Jan 18, 2024. 

5. Cohagan B, Brandis D. Torsade de Pointes. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2023 Aug 8.

6. Li D, Chai S, Wang H, et al. Drug-induced QT prolongation and torsade de pointes: A real-world pharmacovigilance study using the FDA Adverse Event Reporting System database. Front. Pharmacol. 2023 Dec 21; 14: 1259611. DOI: 10.3389/fphar.2023.1259611. PMID: 38186652; PMCID: PMC10771307. 

7. Krumpholz L, Wiśniowska B, Polak S. Correction to: Open-access database of literature derived drug-related Torsade de Pointes cases. BMC Pharmacol. Toxicol. 2022 Jan 31; 23(1): 11. DOI: 10.1186/s40360-022-00550-0. Erratum for: BMC Pharmacol. Toxicol. 2022 Jan 10; 23(1): 7. PMID: 35101142; PMCID: PMC8802418. 

8. Qu Y, Kirby R, Davies R, et al. Time Is a Critical Factor When Evaluating Oligonucleotide Therapeutics in hERG Assays. Nucleic Acid Ther. 2023 Apr; 33(2): 132–140. DOI: 10.1089/nat.2022.0043. Epub 2022 Dec 26. PMID: 36576986; PMCID: PMC10066779. 

9. GLP hERG Screening. 2023. 

10. Zeng H, Kang J. In vitro Testing of Proarrhythmic Toxicity. In: Zhang D, Surapaneni S, eds. ADME-Enabling Technologies in Drug Design and Development. Hoboken, NJ: John Wiley & Sons; 2012. 

11. Masterton J, Tokar S, Jinat A, Kirby R. (2024). GLP hERG Assay Validation following ICH E14/S7B 2022 Q&A best practice guidelines. Posted 2024 Feb 1. Accessed 2024 Apr 2. 

12. Shah RR. The significance of QT interval in drug development. Br. J. Clin. Pharmacol. 2002 Aug; 54(2): 188–202. DOI: 10.1046/j.1365-2125.2002.01627.x. PMID: 12207642; PMCID: PMC1874403.

13. Turner JR, Karnad DR, Cabell CH, Kothari S. Recent developments in the science of proarrhythmic cardiac safety of new drugs. Eur. Heart J. Cardiovasc. Pharmacother. 2017 Apr 1; 3(2): 118–124. DOI: 10.1093/ehjcvp/pvw045. PMID: 28363206. 

14. Callahan T. The Future of QT in Clinical Trials. App. Clin. Trials. 2014 Sept 22. 

The post Advanced In Vitro Screening of New Drugs for Proarrhythmic Activity appeared first on GEN - Genetic Engineering and Biotechnology News.

Moreover, not every E. coli strain/plasmid combination is suitable for every biologic. For that reason, one question has to be answered at the beginning of each production process: Which combination is right for the biopharmaceutical target protein?

Wacker Biotech, a CDMO and subsidiary of the Wacker Group, uses, among other tools, a special expression and secretion system for producing protein-based biopharmaceuticals. Known as ESETEC^®, the patented system was developed on the basis of an E. coli K12 strain and series of highly efficient expression plasmids.

As a result of ongoing optimization, ESETEC now has a toolbox at its disposal which consists of various basic host strains and corresponding expression plasmids. Identifying the best strain/plasmid combination for recombinant production of a pharmaceutical protein at the desired quality and quantity is a bit like looking for a needle in a haystack.

Until now, cell line development at Wacker Biotech was mainly driven by an experience- and knowledge-based approach. Depending on the target protein, certain basic ESETEC host strains and expression plasmids are excluded, while testing is carried out on others. The focus here relies on an initial screening of 10 to 20 combinations of basic host strains and expression plasmids, testing them on a milliliter scale in shake flasks.

Automated analysis

To improve the selection process, the Wacker Consortium, that is, Wacker’s central R&D, developed a multistage high-throughput screening (HTS) system designed to automatically select the right combination of host strain and expression plasmid. The system initially generates up to 300 different combinations. In each case, six host strains are combined with 50 different expression plasmids. Each expression plasmid carries the customer’s gene of interest that encodes for the target protein. Since every plasmid itself has a variety of genetic elements, many different combinations are possible.

The first step is to determine the productivity of each of the 300 different combinations. For a better comparison, three replicates are performed for each combination. Combined with necessary controls, the system simultaneously screens about 1,000 bacterial clones. This wouldn’t be possible without automation. It would take far too long.

Instead of generating and cultivating the different bacterial clones by hand, as was done previously, transformation of different strains with different expression plasmids as well as the initial screening are now performed automatically by multiple robotic systems working on a submilliliter scale. Also, the productivity analyses are carried out with a newly established in-house method, namely, automated RapidFire^® mass spectrometry, which needs just a few seconds per sample.

After the initial screening, the second step is to mimic the typical fermentation environment in miniature bioreactors for the 8 to 16 most promising bacterial clones.

This includes the precise controlling of the pH and the temperature, as well as the supply of oxygen and nutrients. The aim is to figure out which of the selected bacterial clones possess the best production properties under controlled conditions—a step that is also automated at milliliter scale.

Last laboratory step

Once the system has identified the most productive strain/plasmid combination, the team proceeds to the final step: classic fermentation at laboratory scale. They take a closer look at the productivity of the remaining four to eight most promising candidates, studying them on a five-liter scale. In the end, they identify one candidate that yields the best results in terms of quality and quantity.

The system has already demonstrated what it is capable of in actual practice. Multiple test series were conducted to show that automated identification of the most productive strain/plasmid combination works. In one example, the team was able to identify new combinations that did a better job of producing an antibody fragment that Wacker Biotech has used for years as a model protein for research purposes (Figures 1–3).

The process also provided an opportunity to revisit a protein that Wacker Biotech has been producing for a customer for some time: identifying new, more productive combinations yielding an approximately 40% higher product titer. The proof-of-concept was shown. The benefits: high throughput of the HTS system greatly increases the chances of identifying the best combination of host strain and expression plasmids for different target proteins. Plus, not as much time is lost searching for high performers.

Development of the HTS system is ongoing, including, for example, the integration of new host strains and novel expression plasmids. Planning is also underway for using the system to screen for E. coli FOLDTEC^® strains and plasmids, which are key elements of Wacker Biotech’s patented protein refolding technology.

Philipp Schmid, PhD, is a senior scientist, process development, and Marcel Thön, PhD, is a senior expert, bioprocess development, at Wacker Biotech.

The post High-Speed Identification of Superior Production Strains appeared first on GEN - Genetic Engineering and Biotechnology News.

Visualizing metabolites and lipids allows for the connection of the immediate cellular metabolic state with the enzymes and proteins that are doing the work of the cell. MALDI Imaging offers the only unlabeled spatial analysis technique for metabolites and lipids. Additional workflows make released glycans and intact proteins accessible for multiomic connections.

With this technology applied to fresh frozen or FFPE samples, correlating metabolite, lipid, glycan, and protein information to histology becomes increasingly easy at high spatial resolution for faster and more effective analysis of tissue morphological features. This tutorial will highlight the targeted protein workflow portion of MALDI Imaging capabilities, which has recently garnered attention as a breakthrough method for integration across the spatial omics space.

Methods

FFPE human kidney tissues were prepared using the standard MALDI HiPLEX-IHC workflow,^1,2 which is described in Figure 1. (Human kidney tissue was kindly provided by the Medical University of South Carolina.) Briefly, the slides were heated at 60°C and transferred through xylene to a Tris-buffered saline rehydration gradient to remove the wax. The tissue then underwent antigen retrieval in a basic buffer, followed by a tissue blocking step. Next, antibodies of choice (with photocleavable peptide tags) were placed on the tissue and allowed to incubate at 4°C overnight.

The peptide tags were then released using ultraviolet light. MALDI matrix (α-cyano-4-hydroxycinnamic acid) was applied using established protocols on a pneumatic M3+ sprayer (HTX Technologies, Chapel Hill, NC). Finally, recrystallization of the matrix was performed, and the tissue was run on a Bruker timsTOF fleX MALDI-2 instrument at 5 µm lateral spatial resolution using microGRID technology.

After MALDI imaging was performed, matrix was washed off and hematoxylin and eosin (H&E) staining was done in accordance with standard procedures. Data was analyzed in SCiLS Lab software with corresponding H&E staining integrated with pathological annotations corresponding to protein expression for defining key histological features.

Results

Two series of experiments were run for proof of concept of instrument and workflow capabilities. Initial experiments were run with three antibodies on human FFPE tissue at both 20 µm and 5 µm spatial resolution to demonstrate the resolution enhancement and identification of molecular markers for key histological features. Additional experiments were done on serial tissue sections with higher complexity of antibodies to give a more comprehensive picture of protein evaluation. H&E staining was done post analysis and incorporated pathologists’ annotations showing correlation between protein expression and histological features.

The data shown in Figure 2 represent the initial experiments that were done with three different antibodies—specifically, antibodies against vimentin, histone H2A, and ATPase-1A1—to preliminarily evaluate the MALDI HiPLEX-IHC workflow on FFPE human kidney tissues. An image at 20 µm spatial resolution was captured in one area of the tissue, and a subsequent image at 5 µm spatial resolution was obtained in a different area of the tissue. The three peptides associated with the antibodies were at m/z 1222.79 (for ATPase-1A1), m/z 1230.84 (for vimentin), and m/z 1226.82 (for histone H2A).

For maximum clarity in visualization, mass channels for adducts (protonated peptide and sodium adduct) were combined. Closer examination of the 5 µm spatial resolution data is shown in Figure 3. Overlay of the three corresponding masses demonstrated significant localization of the peptides to areas predicted to be rich in the protein of interest (vimentin marker: glomeruli; histone H2A marker: nuclei; and ATPase-1A1 marker: proximal convoluted tubules).

A secondary, higher multiplexed experiment was conducted, including additional markers for CD68 (m/z 1216.75) and collagen 1A1 (m/z 1234.87). The experiment was repeated on serial sections of FFPE kidney tissue and run under the same conditions with two adjacent sections at 20 µm and 5 µm spatial resolution. Both imaging runs successfully localized and identified all antibodies used while containing minimal to no artifacts as a result of the 5 µm spatial resolution. These corresponding protein images were then directly compared to pathologists’ annotations of the histological features of the human kidney tissue (Figure 4).

Conclusion

This work describes the MALDI-HiPLEX-IHC workflow, which can incorporate microGRID and timsTOF flex technology to deliver 5 µm spatial resolution. Other capabilities include the correlation of complex information about intact proteins and the identification of morphological tissue features. In addition, pathologists’ annotations of histologically stained tissues can contribute to analyses performed by the Bruker SCiLS Lab software solution.

Kate Stumpo, PhD, is senior market manager, Imaging Business Unit, Bruker Scientific.

References
1. Yagnik G, Liu Z, Rothschild KJ, Lim MJ. Highly Multiplexed Immunohistochemical MALDI-MS Imaging of Biomarkers in Tissue. J. Am. Soc. Mass Spectrom. 2021; 32(4): 977-988. DOI: 10.1021/jasms.0c00473.
2. Lim MJ, Yagnik G, Henkel C, et al. MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissue. Front. Chem. 2023; 11: 1182404. DOI: 10.3389/fchem.2023.1182404.

The post Advanced Method for High-Resolution Spatial Proteomics appeared first on GEN - Genetic Engineering and Biotechnology News.

Flow cytometry using fluorophore-labeled antibodies has been extensively used to study proteins on immune cells for several decades. More recently, efforts have been made to overcome the multiplexing limitations of conventional flow cytometry by instead labeling antibodies with isotopes for mass spectrometry readout, or with oligonucleotides for next-generation sequencing readout.

Although these approaches can be used to characterize and phenotype cells at high multiplex and throughput, the information they provide pertains only to the abundance of each target protein on each cell. They do not describe the spatial organization of the targeted molecules.

Fluorescence microscopy has traditionally been used to study the spatial organization of proteins on single cells, but multiplexing is limited to a few targets due to the spectral properties of fluorophores, and the signal-to-noise ratio suffers from autofluorescence and spectral bleed-through between channels. Furthermore, the view provided by each microscopy image is limited to a selected focal plane, so if the whole cell surface is to be represented, a Z-stack of images for each fluorophore is required, limiting throughput.

Recently, methods solely relying on oligonucleotide sequences to image biological samples have been demonstrated. Sometimes referred to as “DNA microscopy,” these methods rely on the incorporation of DNA tags that can be decoded to reveal both biomolecule identity and position within the biological sample. These methods offer possibilities to circumvent the limitations in multiplexing, throughput, and (potentially) resolution that beset optical imaging–based methods.

Pixelgen Technologies has developed Molecular Pixelation (MPX) technology to unlock a new spatial dimension to single-cell proteomics research by supplementing abundance information with spatial information about target proteins. This added spatial dimension provides researchers with opportunities to gain deeper insights into cell function at sub-cellular resolution.

The MPX protocol can be performed using standard molecular biology laboratory equipment, without the need for any dedicated hardware or consumables to compartmentalize cells, and a dedicated data processing pipeline is available for DNA processing and analysis of the sequencing output. The reagent kit contains an 80-plex panel against cell surface receptor targets on the major types of peripheral blood mononuclear cells (PBMCs)—T cells, B cells, natural killer cells, and monocytes—and allows for sequencing of up to 1,000 cells per sample and a total of eight samples per reagent kit. Dedicated data processing software tools are available for straightforward data processing and analysis of the rich data that the technology generates.

MPX workflow overview

The MPX workflow can be divided into six steps: a cell preparation step, two pixelation steps, an NGS preparation step, an NGS step, and an analysis step (Figure 1). During the cell preparation step, the immune cells in suspension are chemically fixed with paraformaldehyde to lock the surface proteins in place and prevent any reorganization during downstream sample processing. The fixed cells are blocked, and a target panel of 80 antibody-oligonucleotide conjugates (AOCs) is added, whereupon the AOCs bind their surface receptor targets. Next, the pixelation steps consist of serially hybridizing a set of so-called DNA pixels to the oligonucleotide portion of AOCs bound to cells. DNA pixels are single-stranded DNA molecules produced by rolling circle amplification, where each unique DNA pixel molecule contains repeats of a unique sequence identifier. Each DNA pixel molecule can hybridize to multiple AOCs in proximity on the cell surface.

The DNA pixel identifier sequence is then incorporated onto the hybridized AOC via a gap-fill ligation enzymatic reaction, forming about 1,000 neighborhoods on the cell surface where all AOC molecules within each neighborhood now share the same DNA pixel identifier sequence. The hybridization and gap-fill ligation reactions are then repeated for a total of two pixelation steps, thereby creating two sets of partially overlapping neighborhoods across the cell surface of each assayed cell.

Each generated amplicon contains a protein identifier barcode, a unique molecular identifier sequence, two DNA pixel identifier sequences, and PCR primer sites. The generated amplicons are finally amplified by PCR, purified, and quantified for Illumina sequencing.

Data processing and spatial inference

In short, the dedicated data processing pipeline, which is called Pixelator, receives the sequencing reads and subjects them to quality filtering, decoding (to establish protein identities), error correction, and consolidation (to collapse identical reads into unique sequences). Each sequenced unique molecule can be represented as an edge (link) of a graph (network) with the DNA pixel identifier sequences as nodes and the protein identity tags as edge or node attributes. Separated “cell graphs” representing individual cells are contained within the sample-level graph generated from a sequenced sample.

Spatial inference of the relative locations of individual AOC molecules is possible by interrogating the relative positions of the AOCs within each cell graph. This also allows for the calculation of spatial metrics such as the degree of clustering (polarity) of each of the 80 protein targets, or the level of colocalization between pairs of protein targets.

Results

Data analysis of protein abundance can be performed on MPX data similarly to other multiplexed single-cell methods. For example, PBMCs taken from a healthy donor were processed through the MPX protocol, and then a uniform manifold approximation and projection (UMAP) dimensionality reduction was performed on the protein count matrix output, which formed separated clusters that were consistent with the expected protein signatures for the major cell types expected in the PBMC samples (Figure 2). The fraction of each cell type was also consistent with expected fractions seen in healthy PBMC donors.

To demonstrate the added spatial dimension of the data, Raji B cells were treated with an AOC of the CD20 therapeutic antibody drug rituximab before fixation and processing of the treated cells and untreated control cells through the protocol. Rituximab is known to cluster CD20 on B cells, which should then be reflected in the rituximab polarity score output of data.

The clustering of CD20 occurring upon rituximab AOC treatment was confirmed with fluorescence microscopy (Figure 3). Polarity scores for rituximab depicting the degree of clustered protein expression were compared between stimulated and control samples, and they showed a significant elevation of polarity scores for rituximab-treated cells. Additionally, graph representations of individual rituximab-treated cells, colored by the count density of rituximab of each node, showed a clustered expression pattern consistent with microscopy validation.

Conclusion

Unlocking a new spatial dimension to single-cell proteomics research at high multiplex and throughput can enable researchers to gain additional and deeper insights into immune cell function at scale. Example data from Pixelgen Technologies’ MPX technology showcases the ability to detect differential spatial clustering of a target protein confirmed to be clustered upon stimulation with rituximab.

Filip Karlsson is co-founder and chief technology officer of Pixelgen Technologies. Website: www.pixelgen.com.

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However, many current analytical methods, especially for vaccines and CGTs, face challenges in terms of speed, reproducibility, and resource requirements, driving up costs and development times. Advanced bioanalytics are becoming a vital part of a successful quality by design (QbD) biomanufacturing program, where accurate and precise real-time data enable improved production consistency and product quality.

Implementing process analytical technology (PAT)­—where real-time data, including critical quality attributes (CQAs) and critical process parameters (CPPs), can be comprehensively and proactively monitored and analyzed—allows for advanced process controls and the development of dynamic and robust processes.

A real-time label-free PAT

Laser Force Cytology (LFC), a novel label-free technology, applies optical and hydrodynamic forces to single cells to measure their intrinsic biophysical and biochemical properties without the use of dyes, antibodies, or fluorescent labels.¹ These optical force properties, including refractive index, change with a wide variety of biological phenomena, including cell health conditions, activation, transfection, cell differentiation, and viral infection.

LFC measures subtle early indicators of phenotypic changes and differences in a sensitive and rapid manner, enabling both in-process analytics as well as offline release and potency assays to ensure consistent product quality and yields.^2-4 For example, LFC can provide a coefficient of variation as low as 14% when measuring adeno-associated virus (AAV) transduction.

In this tutorial, select LFC applications illustrate the benefit of real-time optical force data to monitor stem cell differentiation, AAV production via transfection, and live virus vaccine potency. In contrast to many assays that are laborious, slow, and unreliable and thus not suitable as PAT methods, LFC provides accurate, precise, and sensitive results in minutes, demonstrating its key role within QbD programs.

Label-free stem cell differentiation monitoring

Antibodies have wide applicability as analytical tools, including phenotypic cell characterization, protein detection/quantification, and protein separation. However, antibodies are not without their drawbacks, and in many cases, a label-free approach is advantageous. Upon binding to a cell, antibodies can alter its activation state. Consequently, the expression of surface markers is not always consistent, and the analytical results may be affected.

A label-free approach instead allows the cell to be measured in its native state. Antibody sensitivity and specificity can vary based on the target antigen, population diversity, and manufacturing lot, creating false positives and false negatives as well as difficulties in repeating the results of antibody-based studies.⁵ Antibodies also require prior knowledge and the availability of a specific cell surface marker, preventing a priori discovery of unknown changes or differences among cells. In contrast, a label-free approach can make unbiased and universal measurements that are unaffected by lot-to-lot variability.

Finally, antibodies typically require significant time, cost, and labor to implement, making them resource intensive and unamendable for use in PAT methods. LFC provides label-free analysis with minimal sample perturbation and rapid time to result (minutes). One example is monitoring the differentiation of stem cells. LFC data illustrating the differentiation of human bone marrow–derived mesenchymal stem cells (hBM-MSCs) into either osteoblasts or adipocytes are shown in Figure 1.

hBM-MSCs were measured using LFC prior to differentiation and then compared to samples harvested at 7, 14, and 21 days post differentiation for both of the pathways using principal component analysis (PCA).

PCA was used to refactor population wide data from multiple LFC parameters into principal components 1 and 2. In Figure 1, changes are shown for both lineages when compared to undifferentiated cells, with the adipogenic samples showing similar results at days 14 and 21, indicating that differentiation has likely stopped, while the osteogenic samples continue to progress through day 21.

This demonstrates the capability for LFC to monitor differentiation in a label-free manner, providing rapid and sensitive results to inform process development and manufacturing. The ability to quickly obtain nonsubjective results that track differentiation enables real-time process control beyond simple viability and proliferation, without the burden and bias of antibody-based labels.

AAV transfection reagent optimization

The production of viral vectors such as lentivirus and AAV is typically an integral part of the development and manufacturing of advanced therapies such as chimeric antigen receptor T-cell therapies and gene therapies. However, the use of viral vectors faces several challenges related to their development and manufacturing, from characterization, to quantification, to downstream purification.^6,7

Manufacturing, in particular, is challenging when it comes to consistently maintaining high purity, potency, and safety while also focusing on cost controls that are acceptable for large-scale manufacturing.⁸

One of the most common methods of production for both lentivirus and AAV vectors is the use of transient transfection in human (HEK293) cells.9 Current tools to monitor and quantify CQAs such as viral titer during the transfection process are labor intensive and tedious, reducing the speed and efficiency of process development and the ability to monitor in real time.

Shown in Figure 2 are results from a collaboration between Catalent Biologics and LumaCyte to compare AAV vector production using three different transfection reagents, using both LFC and a digital droplet PCR (ddPCR)-based viral genome assay.¹⁰ Transfection complexes were prepared with DNA and with each of the reagents, and then they were added to HEK293 cells.

At 72 h post transfection, cells were harvested and analyzed using LFC and compared to untransfected cells growing in parallel. Figure 2 shows single-cell histograms for each of the reagents compared to the control. For all reagents, the transfection resulted in a broadening of the velocity distribution, indicating an increased population heterogeneity. In addition, the percentage of low-velocity cells increased in the transfected samples, and by defining a velocity threshold of 2,400 µm/s, it was possible for the performance of the reagents to be compared.

As shown in Figure 2, reagent 3 (TR#3) showed the largest response, followed by reagent 1 and reagent 2, respectively.

When velocity data were used, a strong correlation was generated between the LFC measurements, which are available in near real time, and the ddPCR results, which take significant time and labor, demonstrating the strong utility of LFC for rapid process monitoring to improve speed of process development and optimization and ensure manufacturing consistency.

Additional applications of LFC throughout the AAV vector production process include adventitious agent monitoring to rapidly detect potential contamination as well as cell line characterization during process development and scaleup.

Live-virus vaccine production monitoring

The quantification and characterization of viral-based manufacturing processes is an essential component of the production of numerous classes of products, including viral vector vaccines, oncolytic viruses, and live virus vaccines (LVVs). In the case of LVVs, the potency or infectivity is typically the most critical measurement of efficacy. Therefore, real-time potency information from a PAT is extremely desirable during process development and manufacturing. It can increase process knowledge, improve yields, and ensure consistency.

However, existing methods to measure viral potency include the plaque assay and the endpoint dilution assay (50% tissue culture infectious dose, or TCID50), both of which suffer from high variability and long lead times. Thus, they are not capable of serving as PATs. A recent study by McCracken et al.² detailed the use of LFC as a real-time PAT platform to measure LVV potency as well as detect the presence of adventitious viruses.
In one aspect of the study, Vero cells were seeded onto microcarriers, incubated to allow the cells to become confluent, and then infected with attenuated measles virus. At each time point post infection across multiple independent experiments, a sample was withdrawn from the bioreactor and separated into two fractions.

The first contained the microcarriers with cells attached, whereas the second contained any supernatant cells that had detached from the microcarriers. Cell samples were prepared from both fractions and then analyzed using LFC.

In parallel, supernatant samples were analyzed for viral potency using flow virometry as a surrogate measurement for TCID₅₀. Although flow virometry is a physical measurement rather than infectious titer, it was used as an approximate correlation to the TCID₅₀-based potency assay for measles virus during production.

However, should the ratio of total to infectious particles change due to some undetected process perturbation, a cell-based PAT such as LFC would reflect this change while a physical measurement, such as flow virometry, would not.

As shown in Figure 3, a strong correlation was developed between the potency per viable cell and the Radiance infection metric, defined as the percentage of cells with an optical force index greater than 55 s^–1. With this correlation, the absolute average log₁₀ difference between the estimated potency and LFC measurements is 0.074, demonstrating an excellent fit to the data.

Once established, this correlation can be then used to calculate the titer of future production samples in minutes using the LFC data.

Using LFC as a rapid PAT for monitoring potency as well as an analytical assay for measuring infectious titer helps pave the way for reducing the research, development, and manufacturing timeline for LVVs as well as other vaccines that rely on viruses during their development and manufacturing process, including protein subunit vaccines produced in Sf9 cells via baculovirus- or adenovirus-based viral vector vaccines.

The capability to make rapid and precise cell-based infectivity measurements has the potential to improve the entire vaccine development life cycle from R&D to clinical trials and manufacturing, reducing the cost and time associated with LVVs and other viral vaccines.

References
1. Hebert, C.G., et al., Rapid quantification of vesicular stomatitis virus in Vero cells using Laser Force Cytology. Vaccine, 2018. 36(41): p. 6061-6069.
2. McCracken, R., et al., Rapid In-Process Measurement of Live Virus Vaccine Potency Using Laser Force Cytology: Paving the Way for Rapid Vaccine Development. Vaccines (Basel), 2022. 10(10).
3. Hebert, C.G., et al., Viral Infectivity Quantification and Neutralization Assays Using Laser Force Cytology, in Vaccine Delivery Technology: Methods and Protocols, B.A. Pfeifer and A. Hill, Editors. 2021, Springer US: New York, NY. p. 575-585.
4. Bommareddy, P.K., et al., MEK inhibition enhances oncolytic virus immunotherapy through increased tumor cell killing and T cell activation. Sci Transl Med, 2018. 10(471).
5. Baker, M., Reproducibility crisis: Blame it on the antibodies. Nature, 2015. 521(7552): p. 274-276.
6. Clement, N. and J.C. Grieger, Manufacturing of recombinant adeno-associated viral vectors for clinical trials. Mol Ther Methods Clin Dev, 2016. 3: p. 16002.
7. van der Loo, J.C. and J.F. Wright, Progress and challenges in viral vector manufacturing. Hum Mol Genet, 2016. 25(R1): p. R42-R52.
8. Wright, J.F., Transient transfection methods for clinical adeno-associated viral vector production. Hum Gene Ther, 2009. 20(7): p. 698-706.
9. Matsushita, T., et al., Adeno-associated virus vectors can be efficiently produced without helper virus. Gene Ther, 1998. 5(7): p. 938-45.
10. LumaCyte. Radiance® Label-Free Monitoring of AAV Transfection in HEK293 Cells Using Laser Force Cytology (LFCTM). June 6th 2023]

All the authors work at LumaCyte. Colin Hebert, PhD, is senior vice president, scientific and business operations. Mina Elahy, PhD, is a senior application scientist. Sean Hart, PhD, is CEO and CSO; Jonathan Turner, PhD, is an application scientist. Renee Hart is president and CBO.

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With more affordable next-generation sequencing options, single-cell multiomics will increasingly be used as a common approach to profiling cells and tissues. Understandably, the number of single-cell technologies has also rapidly grown in recent years and includes droplet- and microwell-based platforms, as well as microfluidics-free and instrument-free single-cell workflows.

Even with the myriad options for single-cell assays that these technologies provide, researchers are often challenged with technical issues that can impact biological outcomes, such as batch effects or experimental run-to-run variability, sample loss, low cell capture rate, high cell multiplet rate, and high background noise, to name a few. Hence, there is a need for a single-cell platform that can overcome these challenges and provide an accurate and reproducible representation of cellular information in diverse samples, while also providing higher and flexible throughput that can provide cost savings and boost experimental efficiency.

A system for single-cell multiomics analysis

The high-throughput BD Rhapsody HT Xpress System leverages a microwell-based single-cell partitioning technology with minimal benchtop equipment to perform single-cell analysis. With no fluidic pumps or microfluidics, there is no clogging of channels with precious samples. The product features a flexible eight-lane cartridge design with the ability to run up to eight times the number of lanes as the on-market BD Rhapsody Express System with similar workflow time and performance.

With the eight-lane cartridge, one can process up to half a million cells per cartridge. Additionally, in combination with the new BD Flex Single-Cell Multiplexing Kit (SMK), a user can run up to 24 samples per lane or up to 192 samples per cartridge. Single-cell data obtained from each lane is reproducible and concordant between lanes and cartridges, with no lane-to-lane contamination within a cartridge.

A demonstration of system capabilities

Samples stained with different sample tags from an SMK showed no unexpected sample tag data in neighboring lanes (Figure 1). The HT Xpress also features partial use of the cartridge, allowing users to run the unused lanes of the same cartridge at a different time for up to four months.

The HT Xpress offers cell retention of samples with minimal cell loss. Cell capture rates are typically >80%, even with cells of varying sizes and fragility, including neutrophils, T cells, and natural killer cells, as well as nuclei. The system enables the introduction of cells into a cartridge and allows them to settle into individual microwells by gravity. This process results in minimal cell manipulation and assures cell and mRNA integrity.

The stochastic pattern of cells falling into microwells follows a Poisson distribution, which can be used to theoretically estimate the number of wells that contain more than one cell, that is, a multiplet. When a multiplet occurs, the transcriptomes and/or proteomes of two or more cells are captured on a single barcoded bead simultaneously, rendering the data obtained from these cells unusable since the individual cell information cannot be deconvoluted.

The HT Xpress includes a scanner component, that can visualize cells and beads in the wells, providing an empirical estimate of multiplet rates. There is high concordance between both theoretical and scanner estimates of multiplets, even at high cell inputs. In fact, with cell inputs close to 60,000 per lane, scanner multiplet rates have been shown to be reproducibly <10% (Figure 2).

The scanner used in the HT Xpress requires just a field upgrade to the Rhapsody scanner. The scanner enables not only empirical multiplet rate estimates, but also visual quality control (QC) of cell viability and step-by-step QC metrics of the cartridge workflow, including cell and bead capture, wash steps, and bead retention rates.

Implications for scientific discovery

Such a visual in-process QC after single-cell partitioning allows users to make more informed decisions about their experiments prior to library preparation and sequencing, which can save a user a substantial amount of money. Additionally, the biomolecules captured on the beads can be stored for up to four months after the cDNA conversion step, allowing users more flexibility to subsample beads for initial shallow sequencing to evaluate library quality and performance metrics before deciding to potentially proceed with further sequencing of the entire experiment.

The HT Xpress fits within an end-to-end single-cell multiomics solution that is supported by BD Rhapsody single-cell multiomics assay kits and the BD Rhapsody Analysis Bioinformatic Pipeline tool. The pipeline tool generates detailed output files that can be used for comprehensive secondary analyses.

The latest pipeline version also automatically generates a sharable HTML file that highlights QC and summary metrics, with an additional interactive t-distributed stochastic neighbor embedding (t-SNE) plot displaying features such as single bioproduct (gene or protein) expression data and immune cell-type calling information (Figure 3).

Aruna Ayer, PhD, is a senior director, and Cynthia Sakofsky, PhD, serves as a staff scientist at BD Biosciences. To learn more about the BD Rhapsody single-cell multiomics solutions, visit bdbiosciences.com.

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Multiplexed cell and spatial phenotyping of the tumor microenvironment (TME) can provide a deeper understanding of complex interactions between tumors and the immune system, setting the stage for improved patient stratification. Spatial biology provides advantages over other technologies by revealing a clearer and more detailed picture of cellular- and protein-level co-expression, localization, and arrangements within the TME, which in turn can be used to develop prognostic biomarkers called spatial signatures based on the spatial distribution of certain phenotypic features.

PhenoCode Signature Panels simplify spatial biomarker assay development and validation when used in combination with Akoya’s PhenoImager^® platform. Each of the customizable multiplex panels include key markers for comprehensive mapping of the TME and immune status, providing a rapid, quantitative, end-to-end spatial phenotyping workflow.

Fast and flexible PhenoCode Signature Panels

PhenoCode Signature Panels are designed in a customizable format, allowing easy integration of an additional immune cell or checkpoint marker to a preset five-plex panel (Figure 1). Each panel focuses on distinct areas of tumor biology and potential response to therapy that are of greatest interest to translational and clinical researchers. These multiplex immunofluorescence panels combine Akoya’s patented barcode chemistry with Opal-tyramide signal amplification, providing similar accuracy and sensitivity to the gold-standard chromogenic immunohistochemistry. The time required to develop and validate new spatial signatures using the panels is reduced three-fold compared to conventional assay development.

The panels are used in a seven-step procedure to answer key questions and interrogate the TME. The first three steps follow the traditional formal-fixed paraffin-embedded sample preparation, with steps one through three corresponding to slide preparation 
(baking and dewaxing), antigen epitope retrieval, and blocking. In the fourth step, the slides are stained with a primary antibody cocktail in which each antibody has been conjugated to a given barcode.

In step five, a single antibody is revealed at a time, beginning with the hybridization of a complementary oligo barcode conjugated to horseradish peroxidase. Tyramide signal amplification is used in step six to amplify immunohistochemistry detection by covalently depositing an Opal fluorophore near the targeted antigen. Once signal amplification is complete, step seven begins. The horseradish peroxidase–conjugated oligo is dehybridized. Steps five, six, and seven are repeated for each antibody, labeling the markers with the different dyes until all have been revealed.

Spatial signatures for NSCLC

In the study outlined below, a PhenoCode Signature Immuno- Contexture Human Protein Panel was used to accelerate identification of spatial signatures in non-small cell lung cancer (NSCLC) that may reliably predict response to immune checkpoint inhibition.

NSCLC patients can have impaired immune responses within the TME, leading to tumor growth progression and poor prognosis. Accurate cell phenotyping combined with spatial phenotyping can provide a better understanding of complex cellular interactions underpinning the tumor-immune response.

PhenoCode Signature Panels and associated artificial intelligence (AI)-powered image analysis methods were used to identify populations of immune cells and their functional status, as well as their interactions within the TME in a set of NSCLC tissue cores from patients treated with first-line standard-of-care and second-line immuno-oncology treatment. Patient groups included responders (R—full responders, partial responders, and stable disease) and nonresponders (NR).

Formalin-fixed paraffin-embedded NSCLC tissue microarrays (TMAs), comprising n = 38 cores containing a range of carcinomas and pathological Tumor Node Metastasis (pTNM) stages, were stained using the PhenoCode Signature Immuno-Contexture Human Protein Panel. This panel includes markers for T cells (CD8 and FoxP3), macrophages (CD68), checkpoint inhibitors (PD-1 and PD-L1), and PanCK as a tumor marker.

Stained TMAs were scanned at 20× magnification on a PhenoImager HT multiplex imaging system. A total of 36 cores passed image QC and progressed to image analysis. Deep learning algorithms were developed to segment each core into tumor and stroma regions of interest (ROIs) and to accurately detect and classify different cell populations. A DeepLabv3+ neural network was used to develop the classifier using DAPI and PanCK. A customized cell analysis algorithm was trained using a U-Net neural network to detect individual cell lineages and subsequent phenotypes of interest.

A hierarchical approach detected CD8, CD68, tumor cells, and then DAPI cells. Staining variance for CD8, CD68, and DAPI was overcome by generating training labels for the three cell types across the TMA cores and using the three markers as input channels for deep learning training. Spatial analysis was performed using an OracleBio proprietary program to calculate readouts for mean nearest neighbor distances between cell populations, as well as neighborhood analysis for selected phenotypes (Figure 2).

Immune cell counts, phenotypes and spatial interactions were identified within the tumor and stroma ROI per core. Data included total and negative cell phenotype counts, cell density in tumor and stroma, as well as average cell distances between specified phenotypes and neighboring spatial interactions in each of the 36 cores in the TMA set.

Immune cell subsets quantified included FoxP3+, CD8+/PD-1+, CD8+/FoxP3+/PD-1+, and CD68+/PD-L1+. Tumor cells of interest included PanCK+ and PanCK+/PD-L1+. 
Results indicated single FoxP3 per mm2 was significantly lower in the tumor ROI of the R group vs. the NR group (p ≤ .05). A trend was observed in the ratio between CD8 (single and PD-1 dual combined populations) and FoxP3 (single population), where there was a higher proportion of CD8 phenotypes in both tumor ROI and stroma ROI of the R group vs. the NR group. Spatial interactions between phenotypes varied across individual cores, and although trends were observed, no significant differences were found between the R group and the NR group (Figure 2).

The combination of high-quality, spatial phenotyping data provided by the PhenoCode Signature Panel, coupled with deep learning quantitative image analysis techniques, enabled detailed characterization of the complex cellular interactions, at both the functional and spatial levels, within the TME of immuno-oncology-treated NSCLC tissue.

Conclusion

Biomarker discovery based on spatial biology establishes a path toward the use of multiplexed imaging in the clinic, as technologies and workflows become more practical, high throughput, and analytically robust. PhenoCode Signature Panels provide an off-the-shelf, flexible six-plex option that allows more thorough interrogation of the TME with minimal user development requirements.

The ability to deploy signature panels supported by PhenoCode chemistry can accelerate the identification of spatial signatures with the potential to reliably predict response to immune checkpoint inhibition therapy in clinical trials.

Bethany Remeniuk, PhD, is the associate director of laboratory applications at Akoya Biosciences. Nicole Couper is a deputy clinical operations manager at OracleBio. Yi Zheng, PhD, is a director of reagent development at Akoya Biosciences.

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