AI-Powered Label-Free Live-Cell Analysis Outshines Fluorescent Dyes in the Cancer Fight
Fluorescent labels have been a staple in live-cell imaging due to their practical benefits. They make it easy to follow cellular activity, interactions, and proliferation over time, providing valuable data—and stunning images—for many research areas, including cancer.
But what if fluorescence labeling isn’t the best option?
This article is posted on our Science Snippets Blog
The challenge: When cells and fluorescent labels don’t mix
Traditionally, fluorescent reporters have been the common choice for cell analysis; but they bring along some challenges. Labeling protocols can be tedious. But that aside, the real concern is that they can affect your experiment.
For example, labels can alter cell behavior and function, making it hard to interpret your data. They might also induce phototoxicity, which damages cells during the actual imaging. In cancer research, preserving cell integrity is vital for studying behavior, treatment responses, and interactions with the microenvironment.
There is also the challenge of sensitive or rare cell types. Researchers often deal with rare subpopulations of cells, such as cancer stem cells, patient-derived cells, or circulating tumor cells that may be less tolerant of labels.
Do not disturb! Label-free analysis keeps it natural
As we move towards more complex models, the need for non-disruptive, label-free methods has become apparent.
With label-free live-cell analysis, you can observe and analyze cells in a more natural, undisturbed state. There is no chemical perturbation, so you know that the observed cellular behaviors and responses are physiologically relevant.
The big challenges in the field remain in analyzing and extracting information from vast amounts of label-free cell data. There is also the issue of bias. For quality data interpretation, we need standardized and fully objective analysis —something that is beyond the capabilities of humans.
AI is the new ally in label-free image analysis
Incorporating AI into image analysis workflows has opened a world of possibilities, especially for label-free applications. With AI you can quantify a wide range of cell models and make well-informed, data-backed decisions, while minimizing user-introduced bias.
AI tools are based on neural network algorithms, which are much more complex than traditional image analysis. They are trained on millions of cell images and rigorously validated to identify and segment cells based on morphological parameters (size, shape, and texture), often using bright-field microscopy.
To understand the value of AI in discerning cell state and phenotype in a label-free way, let's look at an example in oncology.
AI in action: analyzing chemotherapeutic cytotoxicity
Chemotherapeutic drugs are designed to kill or inhibit the growth of cancer cells. Their success will depend on which surface markers are being expressed by the target cells. Cytotoxicity studies are essential to validate these drugs, find the right dosage, and identify mechanisms of resistance.
In this study, we treated four different breast cancer cell lines with two well-known chemotherapy drugs, Lapatinib and Tamoxifen, and observed the effects using the Incucyte® Live-Cell Analysis System and the AI Cell Health Analysis Software Module.
You can read the figure description for the full details, but in a nutshell, the AI module accurately segmented and classified all four cancer cell lines as live (teal) or dead (magenta). The results were consistent with previous findings:
- BT474 (row 2) and MCF7 (row 3) cells express the markers for both drugs (ER and HER2) and showed cell death in both studies.
- Neither drug worked on the MDA-MB-231 cells (row 4), which do not express these surface markers.
- AU565 cells (row 1) express HER2 but not ER, so as expected, only Lapatinib was able to induce cell death.
Specific cell death induced in breast cancer cells using targeted chemotherapeutics. (A) Images display four breast cancer cell lines untreated or treated with Lapatinib (10 µM) or Tamoxifen (20 µM). Outlines indicate individual cell segmentation as live (teal) and dead (magenta) classification. (B) Time course of cell death in AU565 cells highlights treatment specificity indicating maximal cell death induced by Lapatinib after 3 days, while Tamoxifen has minimal cytotoxicity. (C) Adaptable AI-based analysis enables results to be directly compared across cell lines, yielding insight on compound specificity.
A game changing AI solution for cell health analysis
The Incucyte® Live-Cell Analysis System has been quick to integrate AI-based analysis into its suite of software tools. Its AI Cell Health Analysis Software Module can perform label-free quantification of live or dead cells using trained Convolutional Neural Networks (CNNs). This all happens very fast and with little user input. Here’s how it was made:
- The cell segmentation model was developed using our own images of millions of hand-annotated cells. This data trained the AI to recognize cells in the images, even those that have densely populated areas.
- The live/dead classification model was developed using sets of phase and fluorescence images of both living and dead cells. This allows the AI to determine cell viability using only phase contrast images.
- Next, expert teams of image analysis engineers and biologists tested the models on both adherent and non-adherent cells, to make sure the results check out against established data and traditional fluorescent methods.
We’re just starting to see the potential of what AI can bring to label-free live cell analysis for both adherent and non-adherent cell types. If you’re curious about AI-driven image analysis for cancer cell biology, then check out this short video . Or keep on reading about the AI tools for label-free live-cell analysis on the Incucyte® Live-Cell Analysis System .
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