Cell Viability
As use of physiologically relevant cell models becomes more common, there is a growing need for label-free, non-perturbing solutions that deliver deep biological insights. Fluorescent labeling prolongs workflows and complicates analysis by introducing an additional variable that may influence study outcomes. Eliminating fluorescent reporters altogether ensures experimental observations are not attributed to the label - or to the labeling process itself.
Benefits of Label-Free Analysis:
- A non-invasive, non-perturbing method for studying the health of cell populations when fluorescent labeling is not feasible, such as with rare cell types
- Rapid advancements in live-cell analysis and computational power in recent years can be adapted to describe how complex analysis is distilled into simple, user-friendly workflows. Artificial intelligence (AI) has provided solutions for real-time, label-free analysis of cell behavior and function
- Label-Free Cell Health Assays enable the study of cells in their normal physiological conditions, a critical step when evaluating new therapeutic drug candidates to treat cancer and other diseases
The Incucyte® Live-Cell Analysis System - when used with the Incucyte® AI Cell Health Analysis Software Module for high-throughput, label-free analysis - helps accelerate workflows while generating data that is reproducible, reliable and free of bias.
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Solutions for Label-Free Viability
Software Overview
Incucyte® AI Cell Health Analysis Software Module
The Incucyte® AI Cell Health Analysis Software Module is an all-in-one analysis tool that segments cells and classifies them as live or dead. It allows users to increase throughput of cell health analyses without need for fluorescent labels, generating accurate and objective data with reduced time requirements and cost.
The Incucyte®️ AI Cell Health Analysis Software Module is available to purchase for all S-series instruments and requires a GPU co-processor (BA-04870) installed as a drop-in hardware upgrade to the controller and 2022B software version. Optional further classification of cell populations based on label-free, or fluorescence parameters (fluorescence intensity within cell boundary) is available in this software module.
Incucyte® AI Cell Health Analysis Workflow
Workflow Figure: With the Incucyte® AI Cell Health Analysis, a neural network (pre-trained with validated datasets) informs segmentation and classification algorithms for accurate processing and quantification of live or dead cells. This AI-driven analysis is applied to all wells and timepoints, providing robust data and visualization of live or dead masks.
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Key Advantages of Label-Free Viability
Use AI-driven algorithms to quantify cell viability kinetically based on trained neural networks
Classify live or dead cells and quantify other biological activity within those populations for greater insight
See example data below
Generate robust and accurate results using diversely trained AI-driven analysis on adherent or non-adherent cell types
See example data below
Example Data for Label-Free Viability
Identify and Count Live and Dead Cells Label-Free - Use AI-driven algorithms to quantify cell viability kinetically based on trained neural networks.
Figure 1: Kinetically monitor and quantify drug-treated adherent and non-adherent cells, label-free. HeLa and Ramos cells were seeded into 96-well plates and treated with either camptothecin or Truxima®, respectively. High-definition (HD) phase-contrast images were acquired every 2h over 2 – 3 days, and cell death was quantified using Incucyte® AI Cell Health Analysis. Phase videos show cell death over time (top) and AI-driven Live (green) and Dead (red) segmentation mask outlines (bottom).
Figure 2: Label-free analysis of cell viability. Ramos B-cell Lymphoma cells were treated with increasing concentrations of anti-CD20 antibody Rituximab and the biosimilar, Truxima®. Cell death was quantified using Incucyte® AI Cell Health Analysis. Timecourse data shows that Rituximab and Truxima® induce time- and concentration-dependent cell death, while toxicity induced by IgG control is minimal. Images display Ramos cells at 48h, either untreated (left) or in the presence of 2 µg/mL Rituximab (right). Untreated cells are healthy and classed as Live (green segmentation) while treated cells show partial cell death and are classified as Dead (red segmentation).
Answer Complex Biological Questions Objectively - Classify live or dead cells and monitor cell development steps within those populations for greater insight.
Figure 3: AI Cell Health read-outs are comparable to standard methods. A549 cells were treated with increasing concentrations of camptothecin. Time-courses represent the % Dead cells quantified using label-free Incucyte® AI Cell Health Analysis (left), with the increase in NIR fluorescence due to the use of Incucyte® Annexin V NIR Dye (middle). Concentration response curves show overlay of these data at 72h (right) and IC50 values are comparable
Figure 4: Analysis of cell cycle markers within live cell subpopulation. HT-1080 cells expressing Incucyte® Cell Cycle Green/Orange Lentivirus were treated with a concentration range of camptothecin (4 nM – 3 µM). Incucyte® AI Cell Health Analysis was performed to identify live versus dead cells and fluorescence classification within the live cell population. Images show the classification masks (purple = live, teal = dead) overlaid with green and orange fluorescence channels. Time-courses show fluorescence classification of the live cell population and reveal a time- and concentration-dependent increase in cells in G1 (orange).
Perform Reproducible, High-Throughput Screening - Generate robust and accurate results using diversely trained AI-driven analysis on adherent or non-adherent cell types.
Figure 5. Incucyte® AI Cell Health in 384-well throughput. Four Cell types were seeded into a 384-well microplate and treated with concentration ranges of camptothecin, cycloheximide and doxorubicin. Microplate view shows the increase in % dead cells over 2 days quantified using Incucyte® AI Cell Health Analysis. Data reveals that camptothecin and doxorubicin induced cell death at high concentrations with comparable IC50 values, while cycloheximide had a cytostatic effect at the concentrations.
Label-Free Viability Technical Resources
Featured Resources
Label-Free Viability Frequently Asked Questions
Yes, the AI-based model has been trained to segment in the presence of debris or high plate texture and within reason should be able to mask your cells.
No, the AI-neural network model is pre-trained on a variety of cell types and culture conditions, this allows you to simply perform the analysis with minimal user input.
The AI Cell Mask can be improved using Parameter refinement using Cleanup options (e.g., hole fill) and Filters (e.g., area or eccentricity).
No, customers need to use the AI Cell Scan type at either 10X or 20X objectives.
Yes, the AI Cell Health Analysis can be run on another assay plate and on different cell types if the objective used for acquisition is the same and the plate was acquired using the new AI Scan Type.