Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 25

Real-Time Analysis of Advanced Cell Models | WHITE PAPER

markers, including CD71 (transferrin receptor), is associated with activation as is an
increase in cell size and shape change. To observe these effects at the cellular level
and correlate surface marker protein dynamics with morphology, human PBMCs
were stimulated with anti-CD3/IL-2 (10 ng/ml) or vehicle and monitored over time
(120 h) in the presence of FabFluor-488 labeled anti-CD71. Phase and fluorescence
images were analyzed cell-by-cell to extract information regarding the size distribution, eccentricity (an index of shape) and expression of CD71.
In control, vehicle treated wells, the average cell area and eccentricity of the entire
population at t=0 h was 81 ± 0.5 μm2 and 0.57 ± 0.002, respectively and remained
relatively constant over 120 h in culture. In contrast, following treatment with antiCD3/IL-2, the average cell area and eccentricity increased markedly to 117 ± 4 μm2
and 0.69 ± 0.004 at 120 h, respectively. The shape change was rapid and preceded
the size increase by >24 h. There was greater heterogeneity in the individual cell
morphological parameters of activated cells at the later compared to the earlier time
points, indicating that not all cells responded identically (Figure 6, on the following
page). Following activation, there was a marked, rapid time-dependent increase in
the fraction (%) of cells that were CD71 positive (Figure 7, on page 27). As a control,
the CD4 positive fraction remained relatively constant (15-25%) throughout the du-

A

Figure 5: Immunophenotyping: comparison of IncuCyte live-cell analysis
and flow cytometry. Peripheral Blood Mononuclear Cells (PBMCs) from
three donors were characterized for % expression for 6 CD markers and
IgG control using live-cell analysis and flow cytometry. CD markers
were identified using FabFluor 488-labeled specific antibodies. A strong
correlation was observed between the two methods when considering the
mean values from the three donors (A), or each donor alone (B).

25

| January, 2019

ration of the experiment. From inspection of the cell images and time-lapse movies,
it was clear that the large, less rounded cells were preferentially labeled with CD71
compared to the smaller rounded cells. This was borne out by the cell-by-cell analysis: when cells were classified into distinct subsets based on size (> or <110 μm2) at
48 h, 75 ± 1% of large cells were CD71 positive compared to 12 ± 1% of smaller cells.
By the end of the experiment, >90% of the larger cells were CD71 positive. Together,
these data nicely illustrate the value of independently analyzing subsets of cells,
and demonstrate how cell surface marker expression can be dynamically linked to
morphological change in a cell subset that responds to an exogenous stimulus.

(2) Subset classification based on CD markers:
CD8 positive T lymphocytes
Cytotoxic T lymphocytes are a subset of white blood cells that directly kill target cells
that are either damaged, infected with bacteria or viruses, or recognized as cancerous. These T cells express the cell surface glycoprotein CD8, which is involved in the
recognition of target cells via the T cell receptor/class 1 MHC antigen complex. Typically, between 15-35% of human PBMCs are CD8 positive.

B


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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis

Table of Contents for the Digital Edition of Gain Critical Insights from Advanced Cell Models with Real-Time Analysis

Contents
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 1
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - Contents
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 3
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 4
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 5
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 6
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 7
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 8
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 9
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 10
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 11
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 12
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 13
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 14
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 15
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 16
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 17
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 18
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 19
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 20
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 21
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 22
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 23
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 24
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 25
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 26
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 27
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 28
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 29
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 30
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 31
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 32
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Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 34
Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 35
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