Written by: Tim Bushnell, Ph.D.
I spend a lot of time working with my colleagues and clients retrospectively troubleshooting their data. This involves trying to understand and explain what might’ve happened during the data acquisition that led to the results in question.
During these discussions, I give them tips and strategies to improve their acquisition and data quality over time because we want to make sure that people have the highest quality data they can. This means that the researchers should understand and follow the best practices and are taking the time to make sure that their data is collected correctly.
There are three main areas that you need to think about when troubleshooting your experiments…
1. What do you before you start collecting data?
When you sit down at the cytometer, what are the steps that you go through to make sure the cytometer is ready?
Do you check the quality control logs?
Do you ask the operators for the quality control to see how the machine’s behaving since the last time you ran the system?
Or, better yet, do you have some sort o ...Read More
Written By: Tim Bushnell, Ph.D.
Isaac Newton was famous for saying “If I have seen further than others, it is by standing upon the shoulders of giants.” Implicit in that statement is that the information that the giants provided was reproducible. In fact, reproducibility is central to the scientific method and as far back as the 10th century, the concept of reproducibility of data was being discussed by Ibn al-Haytham.
In 2011, Prinz et al. published an article that indicated a case study looking at reproducibility by Bayer Healthcare found only 25% of academic studies were reproducible. This was followed up in 2012 by a report from Begley and Ellis that indicated on 11% of 53 landmark oncology studies were able to be replicated. So it seems that while we are trying to see farther, our lens may be out of focus.
Bruce Booth, writing for Forbes, published an article called “Scientific Reproducibility: Begley’s Six Rules” and in this article, he proposed the following 6 rules that should serve as a roadmap in evaluating scientific work, both published and your own work. These ...Read More
The Power Of Flow Cytometry In Cell Sorting
This really captures the essence of the sorting process. It is also a very scary process for the average researcher. After spending hours and hours preparing samples, they are often handed off to an operator who puts them into the cell sorter. With fingers crossed, the researcher hopes the cells are sorted correctly, and there are enough of them to perform the downstream experiments that are the real goal of the process.
Most of the time, things go right, but now and then, they fail, sometimes catastrophically. C ...Read More
Walk into any flow cytometry facility and you will see one or more cell sorters. These devices use the principles of flow cytometry to isolate phenotypically defined cells to a high degree of purity for any of a number of downstream applications. Even single cells can be isolated for cloning and single-cell genomics analysis, a very hot area of research these days.
This was not always the case. Prior to 1965, if a researcher wanted to isolate cells, their only choice was some form of gradient centrifugation, a bulk separation method. There were no real options for anything with more fine control.
That changed when Mack Fulwyler published this paper in which he described an instrument capable of measuring an object’s Coulter volume and isolating the cells based on this volume. The ingenious part of the system was the use of the technology that Richard Sweet had developed for the “ink jet oscillograph”. This first cell sorter is shown in Figure 1.
Figure 1: One of the first cell sorters built by Mack Fulwyler.
Fulwyler demonstrated, using a mixture of mouse and human erythrocyt ...Read More
As discussed previously, cell cycle assays require optimization of fixation and dye concentrations, but that is just the beginning. There are important considerations when performing the assay to ensure high-quality data. Cell cycle experiments are judged by the CV of the G0/G1 peak, and the best way to get a good peak is to run the experiment as slow as possible. Likewise, since the cell cycle assay is run with linear amplification, the PMTs must be monitored and their linearity measured.
Even with those 2 aspects on the machine mastered, there are additional details (like synchronizing the cell culture) that need to be considered. Even more so is the fact that the cell cycle assay lends itself to multiplexing, allowing for more information to be extracted from each sample. Those add-ons to the basic protocol need to explored and optimized as well.
Thus, here are 6 areas of consideration for cell cycle analysis covering these important topics.
1. Run cell cycle analysis low and slow
Acquisition of cell cycle data is not like phenotyping. First, data is acquired with linear amplifica ...Read More