Gain Critical Insights from Advanced Cell Models with Real-Time Analysis - 22

Real-Time Analysis of Advanced Cell Models | WHITE PAPER

IncuCyte® Cell-by-Cell: An Overview
IncuCyte® live-cell analysis is now a well-established method for quantifying cell behaviors over time at an industrial scale. In brief, the imaging platform resides within
a standard cell incubator for full environmental control, automatically capturing
and analyzing time-lapse images from up to 2304 assay wells in parallel (6 x 384
well plates). A wide variety of integrated application solutions (software, reagents,
protocols) are available including assays for apoptosis, immune cell killing, neurite
outgrowth, phagocytosis and 3D tumor growth and viability.
To date, all IncuCyte analysis is based on 'whole-image' measures whereby the
information from all objects (cells) in the field of view is consolidated into an average, or aggregate, metric. While this allows for powerful data comparisons from
well to well (as would be applied in drug screening assays, for example), it does not
reliably inform of cell heterogeneity or readily allow cell subsets to be characterized.
Indeed, it is not possible to differentiate a large effect of a treatment on a subset
of cells from a small effect on every cell in the population. Small signal changes in
subsets of cells are potentially masked or overlooked completely by dilution of the
measured signal from non-responding cells. Worse still, an unknowing 'net result
zero' may be returned if two subsets respond in differing directions (e.g. Manshian
et al., 2015). To address this, we introduce new IncuCyte 'cell-by-cell' analysis tools
and labeling methods that combine elements of time-lapse microscopy, HCI and
flow cytometry approaches into an integrated solution.
A central element of the solution is the identification of individual cells using new
image processing algorithms that segment HD phase-contrast images. This permits:
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| January, 2019

A. Phase + Mask

B. Mask

15
40K

C. Phase + Red

D. Red + Mask

Cell count x104

amenable to a single time-point measure, and thus do not easily report population shifts and changes in cell subsets over time. While microscope-based live-cell
imaging methods can address these limitations, the identification of subsets (cell labeling) is typically more problematic in living cells, and only small numbers of cells
(10s) can be routinely quantified. Thus, conclusions about subsets in the population
may be statistically limited. Moreover, environmental control is hard to maintain
on microscope stages for extended periods of time (e.g. >24 h), which prohibits
long term studies. Ideally, large numbers of cells (cf. flow cytometry, HCI) could be
observed with high spatial resolution (cf. HCI, live-cell imaging) over long periods of
time (hours - weeks), using non-perturbing, cell labeling strategies.

20K

10K

5K
0

(h)
Figure 2: Label-free cell counting: validation of cell segmentation Time
method.

IncuCyte S3® Live-cell Analysis System (20x) images of NucLight Red-labeled Jurkat cells: (A)
HD Phase + Phase Mask (B) Phase Mask only (C) Phase + Red Fluorescence (D) Phase Mask +
Red Fluorescence. Scale bar = 50 μM. (E) Time-course analysis of label free cell count (open
symbols) and nuclear count (closed symbols) at different initial cell plating densities (5-40K,
96-well plate). Note the close similarity of values obtained by the phase object and nuclear
label counting methods.

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

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