High-Throughput Screening Tools to Improve and Accelerate Your Cell Line Development

No, Octet cannot separate charge variants of antibody from glycosylation. Octet Glycan screening kits (GlyM and GlyS) are lectin based and recognize specific sugar moieties, but not charge variation.

Yes, you can. This is a typical application in a process screening application and determine most sensitive factors and find suitable ranges. Often, this could be followed up on with more detailed studies using an Ambr^® 250 for easier scale-up.

During ambr15 runs, daily cell sampling is necessary to track both cell count and viability. The iQue^® Cell Count and Viability kit is perfectly designed for the reproducible analysis of cell count and viability to screen and profile cells. The small sample size (1 uL) necessary allows for the preservation of precious sample and this coupled with the streamlined workflow allows for rapid determination of the health of each culture. 

The iQue^® Human IgG Titer and Viability kit  together with the iQue^® 3 High-Throughput Cytometry Platform can be used to calculate titer. One of the key benefits of this kit is that it provides such metrics as IgG quantitation per viable cell. The iQue Forecyt^® software is capable of automatically ranking this data to allow the user to make informed decisions on clone selection.  The detection range of 0.6 ug/mL to 20000 ug/mL makes it more suitable for early-stage cell line development including clone selection. There is also a mouse version of this kit available too. 

Yes, you can. For example, you can also use Protein L-based sensors suitable for certain antibody fragments. In addition, you can quantify certain glycan classes like mannosylated and sialylated species.

Fortunately, for this assay you are mostly using diluted samples (about 10-20x), which reduces the impact of any matrix effects. Still, if you would like to assess any potential impacts, we recommend performing standard addition studies to quantify.

Critical quality attributes (CQAs) of cell lines include identity, microbiological sterility, genetic fidelity and stability, and viability. In terms of characterizing the biopharmaceutical product the CQAs are extended to potency, and biological activity. The later includes the glycosylation pattern, which has implications on the half-life, immunogenicity, and pharmacokinetics of the biologic, and must be closely monitored throughout drug development and manufacturing.

Are you tired of seeding thousands of cells in large stacks of cell culture plates only to find out they are either no single cells, viable or productive? Tired of screening hundreds of plates for the one clone that has it all and will fulfill your projects titer goal? Why leave your pharmaceutical cell line development to chance? With the CellCelector single cell and colony picking platform you can now effectively assess and verify your clones before deciding which ones to choose. Within under a week you will get from a pool of single cells to plates full of clones that have proven to be monoclonal, viable and productive. Instead of relying on large quantities of plates to produce a winner you can now use actual data reliably predicting the future of your clones. This saves consumable and media costs, incubator space but most of all valuable time to reach your project goals by avoiding missteps, a second cloning round and unnecessary procedures.

Automated monoclonality proof: robust single cell detection in nanowells.

In a conventional single cell cloning workflow single cells are seeded in 96 well plates making reliable automated single cell detection in bright field difficult as cells are often settled at the very edge of the well. Thus, the monoclonality status of a given clone at day 0 is usually checked manually or retroactively once the clone has grown. But why search a single cell within a well area that is more than 100 times larger than the surface occupied by a cell? With the CellCelector HT-NIC approach the cells are separated and clearly visible within 200 µm large nanowells and can therefore be identified reliably and automatically by software. Even just after seeding or when they touch the nanowell border.