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Subpopulation Cell-by-Cell Analysis

IncuCyte® Applications

Cell-by-Cell Analysis for Live-Cell Assays



WHITE PAPER: Live-cell Analysis of Cell Subsets and Heterogeneity Download now

Answer more questions about your adherent or non-adherent cell models with IncuCyte® Cell-by-Cell Analysis

Considerable heterogeneity exists in even the simplest of cell systems. With the IncuCyte® S3 and Cell-by-Cell Analysis module, you can quantify the process and effects of cellular heterogeneity in real-time in living cells – inside your incubator. Study the dynamic, phenotypic changes of cell subsets during activation or differentiation, or understand how cell subsets respond to treatments using our unique and accessible approach to live-cell imaging and analysis.

The IncuCyte® S3 Live-Cell Analysis System, Cell-by-Cell Analysis software module, and IncuCyte® reagents provide a new, enabling, end-to-end solution for analyzing heterogeneous cultures at 96-well throughput.

Key Features and Benefits

Feature Benefit
Identify individual adherent or non-adherent cells with advanced software algorithms in high-contrast phase images Label-free quantification of the total cell population
Classification of cells based on morphology or fluorescence with our purpose-built, guided software workflow Easily link cell subset phenotype to function or health, providing complementary functional data to research tools, such as single cell genomics analysis
Proprietary, non-perturbing reagents detect cell surface marker expression, function, or health Enable multiplexed, kinetic analysis in living cells
Cells are kept stationary while optics move with our unique, mobile optical train, permitting observation of shifts in population dynamics in specific areas of the field. Cells are maintained in the field of view during image acquisition
Uninterrupted incubation provided by your tissue culture incubator Reduce artifacts with a consistent, physiologically-relevant environment during the entire experiment
Automated image acquisition and analysis enables high volume data generation in 96-well formats Conduct robust pharmacological analysis. Screen therapeutic drug candidates, or study more variables in less time
Complete end-to-end solution including instrument, software and reagents Spend more time investigating by reducing time spent troubleshooting


Cell-by-Cell Analysis

Gain dynamic insight into the phenotypic biology of subsets of cells

Apply IncuCyte® Cell-by-Cell Analysis to heterogeneous populations such as PBMCs, immune or oncology preparations to study the dynamic behavior of individual subsets of cells, then generate powerful insights by relating data back to disease states, progression, and therapeutic efficacy. Observe differentiation or activation processes dynamically to understand subset modulation and transition over time, and identify therapeutic mechanisms of actions on a specific cell type in a mixed population.

Request an IncuCyte Demo    Request more information

Video Cell-by-cell Subpopluation

Key Advantages

Key Advantages

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Study dynamic changes in cell subpopulations and couple to processes such as activation or differentiation

Quantify cell-specific phenotypic changes in expression markers or morphology within mixed cultures  in an unperturbed micro-environment

Read more below

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Perform kinetic phenotyping to identify subpopulations in mixed cultures

Measure fractional changes in number of cells labeled with your cell surface protein of interest

Read more below

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Gain dynamic insight into drug-induced treatment effects on proliferation

Reveal drug mechanisms of action based on subpopulation studies of cell cycle phase or cell health

Read more below

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Complement your Existing Workflows

  • Easily generate kinetic data from thousands of images using unique Cell-by-Cell image analysis tools and live-cell reagents
  • Study functional kinetic data from identified cellular subsets and compare to total population data

Read more below

Study dynamic changes in cell subpopulations and couple to processes such as activation or differentiation

Quantify cell-specific phenotypic changes in expression markers or morphology within mixed cultures in an unperturbed micro-environment

Figure 1A. Track changes in cellular parameters associated with T cell activation.

Subset classification based on cell enlargement and morphological change in activated T-cells.  PBMCs were treated with anti-CD3 and IL-2, or vehicle control, and monitored over time with IncuCyte® S3.  (A-D)  Activation induces a time-dependent increase in average cell area and eccentricity (all cells).  Note the change in eccentricity precedes the increase in area.  The cell-by-cell area distribution (E) and density plots (F-H) highlight the increased heterogeneity over time following activation, and the appearance of a population of large cells with high eccentricity. Values shown are the mean ± SEM of 12 wells.

Figure 1B. Analyze subsets of activated T-cells: CD71 upregulation.

(A-C) Cell-by-cell density plots shows increase in CD71 (FabFluor-488 labeled) fluorescence as a function of time and cell area (phase) in activated T-cells.  (D) Dynamic changes in the proportion of CD71+, but not CD4+ cells, follows activation. (E) Subset analysis shows preferential upregulation of CD71 expression in large (>100 mm2) vs small (<110 mm2) cells. These data demonstrate an activation-induced, time-dependent increase in CD71 expression and a strong linkage between cell size and CD71, with >90% of large cells expressing CD71 after 4 days of activation

Cell by Cell Subpopulation Figure 1b - Analyze subsets of activated T-cells: CD71 upregulation

Identify subpopulations in mixed cultures with kinetic phenotyping

Measure fractional changes in number of cells labeled with your cell surface protein of interest.

Figure 2a. Immunophenotyping validation via cellular identification and classification on the IncuCyte® S3.

Freshly isolated PBMCs from three donors were characterized for six CD markers and IgG control using either flow cytometry or live-cell Immunocytochemistry analysis using FabFluor-488 labeled Ab.  A strong correlation was observed between two methods when considering the mean values from the three donors (A), or each donor alone (B). 

Cell by Cell Subpopulation Figure 2 - Immunophenotyping validation via cellular identification and classification on the IncuCyte S3

Figure 2b. Phenotyping HER2 expression in various cell types using cell by cell analysis on the IncuCyte S3.

A549, SKOV-3 or SkBr3 cells were characterised for HER2 expression using FabFluor-488 labeled Abs. Images (top row) and quantification of fluorescence (bottom row) demonstrated lack of HER2 expression in A549 cells, heterogeneous expression in SKOV-3 cells and homogenous, high expression in SkBr3 cells. Isotype control responses represent the negative population.

Gain dynamic insight into drug-induced treatment effects on proliferation

Study subpopulations of cells over time based on cell cycle phase or cell health to reveal drug mechanisms of action of cell-type specific cytotoxic effects. Analyze drug-induced treatment effects on cell cycle transitions by tracking phases of the cell cycle. Uncover cell-type specific cytotoxic treatment effect on cell health by labelling a population of interest and multiplexing with readouts of cell health using non-perturbing detection reagents.

Figure 3a. Generate robust cell cycle phase data suitable for pharmacological studies.

Cell cycle phase distribution in HT1080 fibrosarcoma cells expressing the IncuCyte Cell Cycle Red/Green Lentivirus Reagent was quantified following cisplatin or fluorouracil (5FU) treatment. Response at 24h is shown in cell images (outlines show individual cells as identified by IncuCyte Cell-by-Cell Analysis software) (A). Post-cisplatin treatment, percent of cells in the G1 phase (green fluorescence) decreases (B) and S/G2/M phase (red fluorescence) increases (C) in a concentration- and time-dependent manner, in line with its known mechanism of action to interfere with mitosis. After 5FU treatment, the percent of cells in the G1 phase increases (E) and in S/G2/M phase decreases (F) in line with 5FU’s known effect on DNA synthesis. The concentration response curves (D&G) show the compound effects at 24 h on all detectable phases of the cell cycle.

Figure 3b. Track Cell Health in sub populations of cells: Time course of HT1080 fibrosarcoma apoptosis following Camptothecin (CPT, cytotoxic) or cyclohexamide (CHX, cytostatic) treatment.

Cell health was determined with multiplexed readouts of IncuCyte® NucLight Red (nuclear viability marker) and non-perturbing IncuCyte® Caspase 3/7 Green Reagent (apoptotic indicator). Cell images show response at 24 h with associated classification masking (A & E). Cell subsets were classified based on red and green fluorescence using IncuCyte Cell-by-Cell Analysis Software tools (B & F). After CPT treatment, there was a decrease in the red population indicating loss of viable cells, increasing red and green fluorescence indicating early apoptosis, as well as increasing green fluorescence indicating late apoptosis (C). After CHX treatment there was a lack of apoptosis (G). Concentration response time courses of the early apoptotic population are shown (percentage of total cells exhibiting red and green fluorescence, D & H). Values shown are the mean ± SEM of 3 wells.

Complement your existing workflows

Combine the power of automated image acquisition and purpose-built cell-by-cell image analysis tools with unique live-cell imaging reagents to easily generate kinetic data from thousands of images. Study functional kinetic data from identified cellular subsets and compare to total population data.

Quick guide:

quick guide cell-by-cell software


Comparison to traditional technology

The Incucyte® Live-Cell Analysis System and Cell-by-Cell Analysis Software Module provides insight and productivity that compliments established technology for analysis of single cells:

Flow Cytometry High Content Imaging Time-lapse Microscopy

Biological Insight

Extraction of features at individual cell level
Population, Subset & Heterogeneity analysis tools Manufacturer dependent
Live-cell, 'non-perturbing' protocols
(for some applications)
No cell lifting or fixing
Long term, full environmental control
Single-cell tracking Manufacturer dependent
Spatial/Morphological insight - images & movies
Long-term temporal analysis


Microplate throughput
Automated acquisition & analysis Manufacturer dependent

Validation Data

Validation data

Cell-by-Cell Analysis - Figure 4a

Click image to enlarge

Figure 4a. Cells identified in heterogeneous (mixed) cultures and cell by cell analysis. Characterization of a mixed B cell/T cell culture for the CD surface marker CD45 and CD20 using FabFluor-488 labeled Abs. (A) IncuCyte vessel view images (yellow = Ab-labelled cell) of mixed cell populations in the ratios shown. Note the greater proportion of CD20-labeled (Ramos) cells as the ratio of R:J increased in contrast to the CD45 that labels both cell types. (B) IncuCyte cell by cell quantification of the % expression in the mixed culture. (C) Time course of CD20+ cell count showing proliferation of CD20+ cells within the mixed culture. Values shown are the mean ± SEM of 4 wells.

Cell-by-Cell Analysis - Figure 4b

Click image to enlarge

Figure 4b. Label free cell counting of adherent cells using cell by cell analysis. Various densities of A549 NucRed cells were analysed either with cell by cell and red object count to validate label free counting over time. (A) Images demonstrate individual cell masking using the cell by cell software. (B) Time course of phase count and red count across densities shows overlay of data. (C) Correlation of count data over 48 h demonstrates R2 value of 1 with a slope of 1. This has been repeated across a range of cell types. Values show are the mean ± SEM of 4 wells.

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