Articles Written By Heather Brown-Harding

It’s not easy to improve reproducibility in your experiments. 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…

There are 7 different common “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 methods. This is an important concept to grasp because the effects 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.

When you need better resolution than what can be achieved using a traditional microscope, it can be very intimidating to figure out which machines will work best for your experiment. Super-resolution imaging methods require software reconstruction after image acquisition. This is because multiple images are required, and they need to be combined. Additionally, the points of light need to be reassigned to their true location. Today, we're going to discuss 5 different super resolution methods their pros and cons. Although Rayleigh Criterion is not broken, these techniques each feature creative ways to get around it.

When you're performing imaging, always make sure that any phenotype isn't just an artifact of unhealthy cells. If you're doing drug discovery, you want to ensure that the treatment isn't highly toxic to non-target cells. Therefore, it's important to understand the health of the cells.

Live cell imaging is advantageous for research were you may be worried about artifacts of fixation or when you want to measure a phenomenon over time. Live cell imaging is more difficult to achieve than fixed samples because we need to keep the cells live AND happy along with obtaining the images we need. We can reduce artifacts by keeping the cells in a favorable environment and minimizing external stressors. Here are 5 points to keep in mind when setting up your live cell imaging experiment.

Controls are an integral part of all science. And the complexity of fluorescent microscopy makes including the right controls in your experiments paramount. You should be including these 5 controls in your experiments: an unlabeled sample, a non-specific binding control, a positive and negative control, an antibody titration curve, and blinded image capture. With those controls, you can be sure that your experiments are what you think they are and perform your imaging with confidence. So, happy imaging!

Most people are familiar with coverslips being placed on slides to protect the sample, but that's not the only reasons that coverslips are important. They also affect the image quality. Coverslips function by working with your microscope to focus light to a single point and avoiding unnecessary noise in your image. Having the wrong type of coverslip will damage the quality of your images and the quality of the data you extract from those images.