Cell morphology reveals valuable insight into cell health and status, and considerable heterogeneity exists in even the simplest of cell systems. Studying the dynamic, phenotypic changes of cell subsets during activation or differentiation, and understanding how cell respond to treatments are critical elements in improving therapeutic outcomes.
Traditional approaches to cellular analysis involve a multiple step workflow to monitor and quantify the morphological changes of cells or have limitations that require the use of a reagent to measure response to drug treatments. Some of these limitations include:
- Other microscopy based, single experiments require repeated removal of samples from the incubator compromising cell health
- End point analysis using fluorescent dyes may introduce variability in assay results
- Lack of reproducible well-to-well measurements based on a single metric and low data throughput do not scale well for screening
- Involve time-consuming and expensive, manual processes for acquisition of images that require analysis using third party software – necessitating complex data handling and resulting in limited, subjective quantification
Incucyte removes the limitations associated with traditional approaches.
The Incucyte® Cell-by-Cell Analysis Software Module offers label-free cell counts and subsequent cell-by-cell classification of adherent or non-adherent cell models on area, eccentricity, or fluorescence intensity to quantify dynamic changes of subpopulations within mixed cultures.
To further interrogate your cells without labeling, the Incucyte® Advanced Label-Free Classification Analysis Software Module can be added to the Incucyte® Cell-by-Cell Analysis Software Module, expanding the automated identification and quantification of morphological changes. Using Phase HD images, you can specify label-free cells of interest based on their morphology – such as apoptotic cells – and quantify them in real-time. Maximize your label-free analysis with advanced machine learning!
Analysis at the cell-by-cell level promises valuable and additional biological insight beyond that which whole population measurement may deliver. The Incucyte® Cell-by-Cell Analysis Software Module and associated non-perturbing reagents provide automated image capture and objective analysis of populations subsets in real time, providing an integrated solution. Analyze any type of heterogenous culture with microplate throughput!
Introducing Advanced Label-Free Classification Analysis
It is now possible to perform unbiased, automated identification and analysis of cell morphology changes using advanced learning algorithms.
Perform counts and track changes in adherent cell morphology via label-free image segmentation and multivariate analysis of cell shape. Smart default settings allow for repeatable, objective analyses that enable consistent comparison of results across experiments. The Incucyte® Advanced Label-Free Classification Analysis Software Module provides a turnkey solution using an established label-free protocol for acquisition and advanced analysis protocols for simplified identification of target populations based on morphologies.
Incucyte® Advanced Label-Free Classification Analysis Workflow
Workflow Figure: Incucyte® Advanced Label-Free Classification Analysis Workflow shows the steps required to run a label-free live/dead assay. Images are automatically acquired and segmented using the Incucyte® Cell-by-Cell Analysis Software Module. With the Advanced Label-free Classification add-on, morphological features are obtained, and a classifier is trained using live and dead control wells. Classifier is then applied to all wells and timepoints to achieve number and % live and dead cells per image.
- Classify cells automatically and label-free based on morphology - Improve biological insights via reduction in artifacts that can be introduced by labeling reagents using integrated image acquisition and analysis to identify live versus dead or state of differentiation
- Perform unbiased analysis of cell morphology - Achieve repeatable experimental results using powerful, purpose-built software for label-free classification of cell differentiation
- Conduct label-free phenotypic screening - Utilize machine learning for image-based cell morphology analysis in 96- and 384-well formats, unlocking significant throughput
- Attain objective, repeatable results with multivariate algorithms that quantify cell shapes more accurately than a single variable.
Classify cells automatically and label-free based on morphology
Improve biological insights via reduction in artifacts that can be introduced by labeling reagents using integrated image acquisition and analysis to identify live versus dead or state of differentiation.
Figure 1: Advanced Label-free Classification validation Live/Dead Assay. Label-free Live/Dead assays were performed across 6 cell types with a range of morphologies, by treating with concentration ranges of camptothecin (CMP, 0.1 – 10 µM), staurosporine (STP, 1 – 1000 nM) and cisplatin (CIS, 0.5 – 50 µM) in the presence of Incucyte® Annexin V reagent. Images were acquired and segmented using Incucyte® Adherent Cell-by-Cell Analysis Software Module, and Dead cells were quantified using both Advanced Label-free Classification and Fluorescence Classification (Annexin V). Images show the live and dead morphologies of A549 cells treated with CMP, HeLa cells treated with STP, and HT1080 cells treated with CIS. Advanced Label-free Classification time course displays the increase in % Dead cells over 72h, while Annexin V time course displays the increase in % Annexin V positive cells over 72h. Time courses and concentration response curves for both methods are comparable.
Perform unbiased analysis of cell morphology
Achieve repeatable experimental results using powerful, purpose-built software for label-free classification of cell differentiation.
Figure 2: Advanced Label-free Classification validation Differentiation. Label-free Differentiation Assay was performed using THP-1 monocytes which were differentiated to a macrophage phenotype using PMA (100 nM) in the presence of Fabfluor-488 labeled CD11b (a macrophage marker). Images were acquired and segmented using Incucyte® Adherent Cell-by-Cell Analysis Software Module, and Macrophages were quantified using both Advanced Label-free Classification and Fluorescence Classification (CD11b). Phase HD and Fluorescence blended images show the change in morphology and increase in CD11b expression (green fluorescence) as cells differentiate from monocyte to macrophage. Time course of % macrophage displays an increase in differentiated cells over time, where morphological change slightly precedes upregulation of CD11b (0 – 24h). Differentiation plateaus after 48h at a maximal 80% macrophage population using both methods.
Conduct label-free phenotypic screening
Utilize machine learning for image-based cell morphology analysis in 96- and 384-well formats, unlocking significant throughput.
Figure 3: Label-free cytotoxicity screen. Advanced Label-free Classification was used to identify cytotoxic agents. Cells were treated in a 96-well screen with 14 compounds with varied mechanisms of action, including CMP (10 µM, known to induce cell death) as a Dead control. % Dead cells were quantified using Advanced Label-free Classification. Deep View of the 96-well plate shows the Live (green) and Dead (red) classification masks at 72h post treatment, giving users a rapid overview of which compounds and concentrations are cytotoxic.
Attain objective, repeatable results with multivariate algorithms that quantify cell shapes more accurately than a single variable.
Figure 4: Multivariate Analysis increases the accuracy of Label-free Classification. A549 cells were treated with a concentration range of CMP (0.1 – 10 µM) to induce cell death. % Dead cells were quantified using Advanced Label-free Classification of total morphology (multivariate analysis, left) or by using a single metric describing cell circularity (univariate analysis, right); the time courses show % Dead cells over time for each method. Advanced Label-free Classification yields the expected time- and concentration-dependent increase in cell death and this time course is comparable to that of Annexin V validation studies. Univariate analysis of cell circularity shows concentration dependent increase in % Dead cells at 72h however the vehicle indicates a high level of cell death which changes over time; this data is not supported by visual analysis of the images.
Figure 5: Summary table of cell types validated with or without fluorescent reagents and Incucyte® Cell-by-Cell or Advanced Label-free Morphological Analysis. Live/Dead and Differentiation Assays performed with multiple cell types, with or without fluorescent reagents with results validated using Incucyte® Cell-by-Cell or Advanced Label-free Morphological Analysis. Label-free data generated by Incucyte® Advanced Label-Free Classification Analysis confirmed equivalent, reproducible results when compared with the use of fluorescent reagents, assays.
|Incucyte® Cell-by-Cell Analysis Software Module|
Incucyte® Advanced Label-Free Classification Analysis Software Module