Friday, January 4, 2019

How Cell Counts Provide More Context to Your Data


A couple of months ago I, like most of the country, was captivated by the $1.6 billion dollar lottery drawings. After plunking down my $10, I let myself dream about hitting that jackpot and all the trappings of immense wealth. My mind drifted as I could smell the ocean standing on the lanai of my beachside home on some distant Hawaiian Island. Eventually snapped back to reality (Boston traffic does that to you quickly) – I realized I fell into a common pitfall – focusing on a result without accounting for variables. In the case of the lottery, the biggest variable being the odds of winning. With a possible 302 million combinations, the odds of hitting my numbers were about 6 times higher than getting killed by a falling coconut (1 out of 50 million).

All of this discussion of odds got me thinking about data sets I see at our customers’ sites. I often glance at heat maps of raw data as they are generated from our instruments. The high contrasting colors always look promising at first, but unlike the lottery, as scientist we are trained to account for variability. One critical variable in any cell-based assay is that differing numbers of cells between individual assay points can skew data. Typically cell cultures are optimized to account for proliferation rates, rate of cell death, cell adhesion so that cell numbers are consistent before performing any assays. Even with optimized conditions, there can be small variations that can affect the final cell density. These differences can ultimately cause increased variability across replicate data and throughout the experiment. Thus getting an accurate cell count within the assay well would allow for normalization of the data and remove this source of variability.

There are many strategies for normalizing cell population but the preferred method for normalization involves counting fluorescently stained cells in each well of a microplate via microscopy. With BioTek’s imaging and microscopy products, we provide an automated solution for cell counting that historically had been a tedious manual process or required the use of expensive imaging instruments. In the following example we use the nuclear stain DAPI to identify the cell nucleus then BioTek’s Gen5™ software automatically sets a threshold to provide accurate cell counts.

Figure 1: In-Situ Nuclear Staining (A) Threshold Analysis for object selection and count
As a Field Applications Scientist, I see firsthand how this powerful tool helps advance our customers’ research. Earlier this year, we launched a new integrated solution with Agilent Technologies that combined Agilent’s cellular metabolic analysis with BioTek’s imaging technologies. Centered around Agilent’s Seahorse XFe analyzers, customers can monitor real-time oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of live cells in a multi-well plate, interrogating key cellular functions such as mitochondrial respiration and glycolysis. When coupled with BioTek Cytation™ line, we now provide a powerful and intuitive turnkey platform for normalizing metabolic data. Perhaps the most impressive part of the system is how seamless the interface and workflow is for the customer. With a unified software look and feel provided by Agilent (with Gen5 powerful tools running in the background), the image based normalization is initiated with a simple barcode scan and a few confirmation checks. All of the data is centralized into the Agilent Wave software for complete analysis and interpretation. When the Agilent system is not active, the customer can utilize the Cytation as a stand-alone imager for other quantitative microscopy applications.


For more detailed information, see the XF Data Normalization by the Agilent Seahorse XF Imaging and Normalization System Application Note.

By: BioTek Instruments, Jared Amuan, Field Application Scientist

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