High-content imaging has come a long way in recent decades. Advances in imaging systems, software, and data analysis techniques now make it possible to study increasingly complex biological samples, providing valuable insights into cellular behavior and drug responses. However, despite these innovations, misconceptions about high-content imaging remain surprisingly common in research labs.
Many of these myths arise from outdated perceptions of legacy systems, while others are fueled by the belief that collecting more data automatically equates to better insights. In this blog, we explore four of the most common myths surrounding high-content imaging and the facts that dispel them.
Myth: Lasers are required for high-quality confocal imaging.
Reality: Image quality depends far more on the optical architecture and the light path length than on the type of light source used. While lasers are often associated with confocal imaging, high-performance imaging can also be achieved using advanced LED technology.
Myth: More lasers are always better for higher multiplexing.
Reality: Adding more laser lines does not necessarily translate to better imaging. Modern confocal microscopy systems can achieve excellent multiplexing quality with fewer, carefully selected wavelengths. In fact, increasing the number of laser lines can actually increase system complexity, maintenance requirements, and cost without significantly improving image resolution or contrast.
Myth: More data means better results.
Reality: Capturing more images doesn’t necessarily guarantee better insights. Best practices prioritize data quality over quantity, focusing on optimizing key parameters such as magnification, fields of view, and z-stacks. By tailoring these to the assay’s requirements, researchers can achieve efficient data collection and generate more meaningful results.
Myth: AI will solve all my image analysis challenges.
Reality: While AI can be a powerful tool for image analysis, helping researchers segment images, analyze large datasets, and streamline workflows, it should always be used strategically. Many AI systems function as ‘black boxes’, offering limited transparency into how conclusions are reached. Additionally, machine learning models depend on training data that, if not carefully monitored, can introduce biases and lead to skewed or misleading results.
Together, these misconceptions can lead to inefficient workflows, increased costs, and, potentially, missed research opportunities.
Are you ready to separate fact from fiction?
For more in-depth analysis of these high-content imaging myths, plus detailed technical explanations and practical solutions, download our full myth-busting article.
For Research Use Only. Not for use in diagnostic procedures.
