Written By: Heather Brown-Harding, PhD
Image manipulation has become a major problem in science, whether intentional or accidental. This has exploded with the advent of digital imaging and software like Photoshop. There are even mobile applications like Instagram filters that can be used for imaging trickery. It should go without saying that image reuse/manipulation represents profound dishonesty in science – a field intended to uphold the most stringent possible standards of truthful inquiry! But what about studies with a sloppy or stunted capacity for reproduction? These, too, plague science and hinder our ability to seamlessly move forward – irreproducible research demands surplus time and energy from the scientific community and must, therefore, be addressed.
Have you ever tried to reproduce results from another lab only to discover it didn’t work as you expected? What about results from your own lab? Maybe there isn’t a standard operating procedure in place, or maybe this procedure has changed over time (yet no one has bothered to document these changes t ...Read More
Written By: Tim Bushnell, Ph.D.
Putting it basically, compensation is the mathematical process of correcting spectral spillover from a fluorochrome into a secondary detector. Yet even the most talented rookie researchers may find themselves at a loss when it comes to this topic. Compensation is one of the most important parts of the experiment to get correct, and yet there remain rumors and myths that circulate among users that will prevent you from getting correct compensation.
Good compensation requires that you tightly adhere to certain rules, understand the function of your instrument, and keep in mind how fluorescence occurs. Faulty compensation leads to false conclusions, and you certainly don’t want that.
Even among us flow cytometry veterans, a strong foundation is occasionally in need of a tune-up. And in a topic as dense as flow cytometry, it’s important that we refresh ourselves on some of the fundamentals once in a while. In fact, it is the longtime cytometry expert who must check themselves for any sort of faith in faulty old compensation practices. Science is ever ...Read More
Written By: Heather-Brown Harding, PhD
There are 7 different “artifacts” that may be affecting the quality of your imaging. Before digging into the details, let’s begin by defining an artifact: Essentially, it is any error introduced through sample preparation, the equipment or post-processing. This is an important concept to grasp because the effect of artifacts can cause false positives or negatives, and they can physically distort your data. This is, of course, at odds with your mission to obtain reliable quantitative data. So what can you do to stop these artifacts? The problems can range from dirty objectives to bigger issues like light path aberrations. We’re going to discuss 7 individual artifacts and how to minimize them.
1. Sample preparation.
Sample preparation presents a few different opportunities for artifacts to arise. Air bubbles are a problem because they cause a difference in brightness through light distortion. This occurs due to the differences in refractive index bending light differently through the sample and back to the objective. Another common sample ...Read More
Written By: Tim Bushnell, Ph.D.
As a follow-up to our post on tSNE where we compared the speed of calculation in leading software packages, let’s consider the case of SPADE (Spanning-tree Progression Analysis of Density-normalized Events). A favored algorithm in the flow cytometry community, SPADE is used when dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format.
Unlike tSNE, which is a dimensionality-reduction algorithm that presents a multidimensional dataset in 2 dimensions (tSNE-1 and tSNE-2), SPADE is a clustering and graph-layout algorithm. The result is quite different from the two-dimensional plot of tSNE, and rather resembles a phylogenetic tree in its branching structure (Fig. 1). As with a phylogenetic tree, similar clusters are grouped closer together, and dissimilar clusters are located more distally on the tree. If you’d like to read more on the theory and development of the SPADE algorithm, see the original literature [Qiu ...Read More
Written By: Tim Busnell, Ph.D.
1. How it works
2. Panel design
3. Sample preparation
4. Data analysis
5. Imaging mass cytometry
Today’s article will summarize the functionality of mass cytometry technology. This tech has been commercialized largely by Fluidigm in the CyTof systems. There are 5 key points to cover, or takeaways, that cytometrists should keep in mind as they perform their research.
How Does Mass Cytometry Work?
Traditional fluorescent flow cytometry has started to push the limit of the number of simultaneous parameters that can be measured. With the recent advent of spectral cytometry, as many as 40 simultaneous fluorescence parameters can be measured.
The first foray into high-dimensional cytometry didn’t use fluorescence. Rather, the antibodies were labeled with metal ions. To measure these labels, the cells had to be vaporized and the ion masses measured using a different detector. Thus cytometry time-of-flight, or CyTOF, more commonly known as Mass Cytometry, was born.
Figure 1: The CyTOF process, from Bendall et al. (2012).
The mass cytometry process is sh ...Read More