Aggregated Cell Count Assay: A Challenge in Cell Culture

Cell culture is a primary process in biology studies. It helps us understand how cells work, how they react to drugs, and even how to work with tissue. But there’s a problem with accurately counting and checking the health of clumped-together cells.

Some cells, like sphere cultures, induced pluripotent stem cells (iPSCs), and certain cancer cell types, clump together. This makes using normal counting methods tricky. That’s where the aggregated cell count assay comes in. Let’s examine how this assay operates and why it’s so vital in cell culture research.

The Challenge of Counting Aggregated Cells

Counting cells that are clumped together is a challenge. Standard counting methods, such as a hemocytometer with trypan blue, need cells to be apart. But when cells stick together into clumps or multi-cell spheroids, they cover each other, making it hard to see and count them right.

Some cell types, like MCF-7 breast cancer cells or CHO-S cells that are used to make biopharmaceuticals, are even worse. They often group together tightly or grow on tiny support particles.

The Cell Counter Solution

The cell counter introduces a fresh solution. Instead of counting whole cells, it concentrates on tallying up nuclei. This technique uses a simple two-step process. It guarantees an accurate cell count and viability evaluation, even with incredibly clumped-together samples.

Step One: Lysing and Staining

First, the aggregate gated Cell Count Assay begins with every cell in the sample bursting open with a special mixture. This action tears apart the cell covering, freeing the nuclei into the liquid. Next, the sample gets marked with DAPI. This glow-in-the-dark DNA marker attaches only to the nuclei. Since the nuclei take up just a bit of the total cell space, they’re less likely to stack on top of each other, resulting in a more precise count.

Step Two: Viability Assessment

We move to step two, finding the count of dead cell cells. After breaking them down, we use DAPI to stain another sample. The cell counter scans the sample fast and gives us an accurate count of all the cells and dead ones in just half a minute. This swift, automatic method ensures that researchers get solid data, leaving out any inconsistencies coming from counting by hand.

The Problem with Aggregated Cells

Cell clusters, or aggregated cells, happen when cells stick together. They can be a few cells or many, making it difficult to count each cell. Old-fashioned methods, like a hem cytometer with trypan blue, count cells visually. But this is super hard when cells overlap and hide within clusters. This trouble is even worse, with specific cell types often sticking together, like MCF-7 breast cancer cells or CHO-S cells for medicine production. These cells tend to form compact groupings or grow on tiny carriers, making accurate counting with common techniques hard.

The Nature of Aggregated Cells

Cells sometimes stick together, forming clusters called aggregated cells. This may be intentional, like creating 3D clumps for lab models, or unintentional, like cancer cells grouping together. The driving force behind this is cell recognition and sticking to each other. While the process has benefits for mimicking real-life situations, it can make it tricky to count cells accurately and check their viability.

Specific Challenges in Counting Aggregated Cells

Cell Clusters: When cells group together, it’s hard to tell one from the other using a microscope. Old-school tools like a hem cytometer with trypan blue staining want cells to stay separate, but when cells bunch up, it’s nearly impossible.

Different-Sized Clusters: Small and big cell clusters can be found in one sample. This makes counting cells confusing because one part of the sample might have more cells if the clusters aren’t spread out evenly.

Mistakes in Manual Counting: Manually tallying cell groups can lead to mistakes and inconsistent outcomes. It’s a long process and can be tricky when clusters are tight.

Issues with Checking Cell Health: Gauging the health of cells in clusters is not always accurate. If live cells cover dead ones in a cluster, the count can be off. Traditional methods like trypan blue staining struggle with dense clusters, giving skewed results.

Automated Counter Concerns: Some high-tech counters use bright-field imaging, but they still have problems. They either count clusters as single units or can’t tell apart live and dead cells in clusters.

Conclusion

The Aggregated Cell Count Assay, powered by cell culture instruments, represents a significant advancement in cell culture research. By addressing the challenges posed by highly aggregated cells, this method provides researchers with the tools to obtain accurate, reliable, and consistent data. Whether in cancer research, stem cell studies, tissue engineering, or biopharmaceutical production, the ability to accurately count and assess cell viability is crucial.