Rapid, Massively Parallel Single-Cell Drug Response Measurements via Live Cell Interferometry

California NanoSystems Institute, David Geffen School of Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California, USA.
Biophysical Journal (Impact Factor: 3.97). 09/2011; 101(5):1025-31. DOI: 10.1016/j.bpj.2011.07.022
Source: PubMed
ABSTRACT
A central question in cancer therapy is how individual cells within a population of tumor cells respond to drugs designed to arrest their growth. However, the absolute growth of cells, their change in physical mass, whether cancerous or physiologic, is difficult to measure directly with traditional techniques. Here, we develop live cell interferometry for rapid, real-time quantification of cell mass in cells exposed to a changing environment. We used tunicamycin induction of the unfolded protein stress response in multiple myeloma cells to generate a mass response that was temporally profiled for hundreds of cells simultaneously. Within 2 h, the treated cells were growth suppressed compared to controls, with a few cells in both populations showing a robust increase (+15%) or little change (<5%) in mass accumulation. Overall, live cell interferometry provides a conceptual advance for assessing cell populations to identify, monitor, and measure single cell responses, such as to therapeutic drugs.

Full-text

Available from: Thomas Zangle
Rapid, Massively Parallel Single-Cell Drug Response Measurements
via Live Cell Interferometry
Jason Reed,
* Jennifer Chun,
Thomas A. Zangle,
§
Sheraz Kalim,
§
Jason S. Hong,
§
Sarah E. Pefley,
Xin Zheng,
§
James K. Gimzewski,
{
and Michael A. Teitell
†‡§k
*
California NanoSystems Institute,
Bioengineering Interdepartmental Program,
§
Department of Pathology and Laboratory Medicine, David
Geffen School of Medicine,
{
Department of Chemistry and Biochemistry, and
k
Center for Cell Control, Broad Stem Cell Research Center,
Jonsson Comprehensive Cancer Center, and Molecular Biology Institute, University of California, Los Angeles, California
ABSTRACT A central question in cancer therapy is how individual cells within a population of tumor cells respond to drugs
designed to arrest their growth. However, the absolute growth of cells, their change in physical mass, whether cancerous or
physiologic, is difficult to measure directly with traditional techniques. Here, we develop live cell interferometry for rapid, real-
time quantification of cell mass in cells exposed to a changing environment. We used tunicamycin induction of the unfolded
protein stress response in multiple myeloma cells to generate a mass response that was temporally profiled for hundreds of cells
simultaneously. Within 2 h, the treated cells were growth suppressed compared to controls, with a few cells in both populations
showing a robust increase (þ15%) or little change (<5%) in mass accumulation. Overall, live cell interferometry provides
a conceptual advance for assessing cell populations to identify, monitor, and measure single cell responses, such as to thera-
peutic drugs.
INTRODUCTION
Interference microscopy is an interesting biophysical
approach to measure the spatial distribution of material
inside cells and other transparent objects. We have previ-
ously shown that an adaptation of this technique, which
we call live-cell interferometry (LCI), can sensitively detect
and track the nanomechanical properties of hundreds of
cells simultaneously (1). LCI can also be used to monitor
the dynamic flow of the cytoplasm inside single cells as
small indentions are made by highly magnetic probes on
the surface of a cell (2). It was determined that an almost
instantaneous redistribution of cell material resulted from
indentation of the cell surface, which was beyond the detec-
tion limit of conventional optical microscopy. These results
suggested that changes in cell mass could serve as a sensi-
tive, real-time, and noninvasive marker of cellular fitness.
If conducted in a highly parallel fashion, mass measure-
ments could become an effective mechanism for profiling
the differential response of cells in a population to internal
or external stimuli.
How individual cel ls regulate their size is poorly under-
stood, as is the relationship between cell mass and well-char-
acterized biochemical pathways. Although quantitative mass
measurements of single live cells began in the 1950s (3,4),
only recently have newer approaches to increase the speed,
precision, and practicality of cel lular mass measurements
become available. For example, a microelectromechanical
systems-based approach has been developed to sensitively
measure mass changes in a variety of nonadherent cell types
flowing sequentially through a hollow microcantilever
resonator (5,6). Our own studies with LCI, and the work
of others using optical coherence-based microscopy (7,8),
suggest a different and powerful approach for rapidly, simul-
taneously, measuring the masses of individual cells within
large populations of cells.
The physical principal underlying LCI is as follows: The
variation in phase imparted to coherent or semicoherent
light propagating through a transparent cell body is linearly
proportional to the material density of the cell (9–11). Inter-
ference microscopy can measure these changes in phase, for
micron-sized objects, to a precision exceeding 1/1000 of
a wavelength, or better than 0.5 nm for visible light. Cell
mass can then be related to the measured phase retardation
of each cell as (9) m ¼ 1=a
R
fl dA ;where m is the mass of
the cell, a is a constant describing the relationship between
phase shift and cell mass, f is the measured fractional phase
shift, l is the illumination wavelength, and integration is
performed across the entire cell area, A. Here, a ¼ 1.8
10
3
m
3
kg
1
, consistent with Ross (9) as an average value
taking into account the usual contents of a cell. The exact
value of a is not known, however, based on previous inde-
pendent measurements, it is assumed that: 1), a remains
constant across a wide range of concentrations and 2), a is
not likely to vary > ~5% due to changes in cellular content
(9,11,12). Nevertheless, the specific value of a will not
affect the accuracy of measurements of comparative growth
rates (c.f. Figs. 2 and 3) and relative daughter cell masses
after cell division (c.f. Fig. 5). Fig. 1 shows a schematic of
the LCI, and typical optical thickness images of adherent
and nonadherent cells.
Because it is a wide-field imaging technique, LCI
provides simultaneous mass measurements of hundreds of
cells (Fig. 2). Throughout the data collection, cells can be
Submitted May 14, 2011, and accepted for publication July 18, 2011.
*Correspondence: jreed@cnsi.ucla.edu or mteitell@ucla.edu
Editor: Denis Wirtz.
Ó 2011 by the Biophysical Society
0006-3495/11/09/1025/7 $2.00
doi: 10.1016/j.bpj.2011.07.022
Biophysical Journal Volume 101 September 2011 1025–1031 1025
Page 1
maintained in standard culture dishes in physiological
conditions (e.g., pH 7.4, 37
C, 5% CO
2
) enabling periodic,
longitudinal measur ements for 6 h or longer (Fig. 2 a). With
an automated image processing algorithm, hundreds of cells
can be identified and mass profiled in each image in rapid
succession (Fig. 2 b). In these conditions, the single-cell
mass measurements are highly repeatable (<3% CV (coef-
ficient of variation); see Methods: measurement errors). At
each time point, therefore, the population-wide distribution
of cell mass can be determined (Fig. 2 c). Furthermore,
FIGURE 1 Live cell interferometer (LCI). The LCI (a)
is a Michelson-type interference microscope that
compares the optical thickness of a reference cell to the
optical thickness of samples placed in the observation
chamber. Suspended in the observation chamber is
a mirrored substrate, allowing the LCI to make measure-
ments of optical thickness on transparent cells. The rela-
tive position of the microscope objective and observation
chamber is controlled by computer and translatable in
three-dimensions allowing for rapid, automated image
acquisition. Throughout data collection, cells in the obser-
vation chamber are maintained in standard cell culture
conditions (e.g., pH 7.4, 37
C, 5% CO
2
). The LCI is
capable of measuring the mass of both adherent and non-
adherent cells. Frame (b) shows several nonadherent H929
cells attached to the observation chamber substrate after
coating the substrate with Poly-L-Lysine solution, whereas
frame (c) shows adherent female Indian Muntjac (9) cells
cultured directly on the substrate. The color maps show
optical thickness measurements with blue being a low
optical thickness relative to background and red being
a high optical thickness.
FIGURE 2 LCI enables high-throughput and
longitudinal measurements of cell mass: Four
sample images of H929 multiple myeloma cells
(a) from the LCI show optical thickness profiles
of cells over 6 h of monitoring. Color indicates
the phase shift in nm, with dark blue indicating
low thickness and white/red indicating high thick-
ness. These sample images are composites of 25
successive charge-coupled device captures taken
every 7 min. The inset shows a measurement of
the phase shift across a single cell. Integrated phase
shift across a cell is directly proportional to cell dry
mass. (b) Hundreds of individual cells (outlined in
red) are identified at unique positions in each
frame and (c) the mass of each individual cell is
determined, enabling high-throughput, popula-
tion-level mass profiling over time. (d) The mass of individual cells is tracked longitudinally over time to examine single-cell growth dynamics. Measure-
ments are shown as open symbols with a linear least squares best fit line. The measured growth rate in this case is 6.5 (se 5 0.72) pg/h. The variation about the
linear trend, taken as the standard deviation of the residual error, is 5.0 pg or 1.17% of the median cell mass. The maximum peak-to-peak residual error is
11 pg at 102 min, or 2.61% of the median mass at that time point.
Biophysical Journal 101(5) 1025–1031
1026 Reed et al.
Page 2
individual cells can be tracked over long periods of time to
yield growth rate curves (nonaqueous cell mass changes), as
in Fig. 2 d.
MATERIALS AND METHODS
Interferometer
The live cell interferometer has been described in detail previously (1).
Briefly, the system is an optical microscope, based on a modified Veeco
NT9300 optical profiler, with a 20X 0.28NA Michelson interference objec-
tive that allows for the observation of not only lateral features with typical
optical resolution (1.16 mm for the 20 objective) but also height dimen-
sions of reflective objects below the scale of 1 nm. The Michelson interfer-
ometer is composed of a beam splitter, reference mirror, and compensating
fluid cell to adjust for optical path differences induced by fluid surrounding
the specimen. The phase shifting interferometry (PSI) (14) method was
used to capture phase images of the cell bodies in situ. During measure-
ment, a piezoelectric translator decreases the light path a small amount
causing a phase shift between the test and reference beams. The system
records the irradiance of the resulting interference pattern at many different
phase shifts and then converts the irradiance to phase wavefront data by
integrating the irradiance data using a PSI algorithm. As currently imple-
mented, the autofocus and PSI measurement cycle takes 12 s. The PSI
measurement itself takes 1–2 s, and is limited by the camera frame rate
(60 fps). In our present experiments, one set of 25 images, containing
400–1000 cells, was captured every 7 min. Each set of 25 images contained
hundreds of cells, with data from the first five images presented here, and
therefore each run includes ~80 cells. All cells within each of the selected
images were measured.
Data analysis
The software native to the Bruker NT9300 (Bruker, Tucson, AZ) allows
automated optical thickness measurements of cells selected manually
from the phase image. The optical thickness is converted to mass as described
in the text, using the conversion constant, a ¼ 1.8 10
3
m
3
kg
1
, consistent
with Ross (9). The boundary of each cell was automatically selected by an
algorithm that partitions objects from the background using a threshold
determined from the histogram of pixel heights (15). Conversion of the
raw phase image into optical thickness uses a series of well-established
phase unwrapping routines (16). Occasionally, this conversion from phase
to optical thickness is incorrect by a factor of negative one wavelength
(530 nm), which causes contiguous regions with the cell to have an apparent
optical thickness one wavelength less than the true value. This error is
easily detected as a nonphysical discontinuity in optical thickness, and
corrected by adding back one wavelength of optical thickness to the affected
pixels. This process is not currently fully automated.
Quantification of measurement errors
The accuracy of interference microscopy for cell mass measurements is
firmly established in the electromagnetic theory (17,18), and by a variety
of reference techniques that include ultracentrifugation (3,4,10–12,19–
21), refractometry of protein solutions, hydrogels, and transparent films
(22–24), x-ray densitometry (25), and electron microcopy (26–30). To char-
acterize the accuracy and stability of our LCI system, we conducted several
benchmark experiments, the detail for which is given in the Supporting
Material, see Fig. S2, Fig. S3, Fig. S4, and Fig. S5. The lower limit of
CV for LCI mass measurements, which is a function of the temporal
stability of the interferometric optical path (1.2 A
˚
; Fig. S2 a), was deter-
mined to be ~0.35%. Similar CVs were determined for serial measurements
of partially melted polystyrene beads, which simulated cells (CV < 0.4%;
Fig. S2 b), and for short repeated measurements of actual live cells (CV <
1%; Fig. S3). We measured populations of 6 mm diameter polystyrene
spheres (Fig. S4 a) normally used as calibration standards in flow cytometry
(Flow Check, Polysciences), and for which a population mean volume and
standard deviation are provided by the manufacturer; the population mass
CV determined by LCI (6.8%) was considerably smaller than that deter-
mined by the manufacturer (15%). We also measured a population of red
blood cells (RBCs) freshly obtained from a 15-week-old female C57BL/6
mouse (Fig. S4, b and c). Mouse RBCs serve as an informative independent
standard because there exits an established range of values for average cell
mass (determined by photochemical and other methods). Our LCI-deter-
mined value of mean RBC cell mass, 19.4 pg, are in excellent agreement
with the range of published values at 15–21 pg (9–12,31). Finally, for
comparison we measured the masses of populations of a variety of mamma-
lian cell types (Fig. S4 b and Fig. S5). These are plotted together with the
mouse RBC and polystyrene sphere data in the Supporting Material and
Fig. 3 b. To estimate the scale of measurement variation in multihour live
cell experiments, all single-cell mass versus time data, (representing
~480 cells) were fitted to a simple exponential growth model (mass(t) ¼
m
0
*C
t
, where the constant C is close to unity) and residual error calculated
as the percent difference between the trend and the actual data at each time
point (Fig. S6 a). The residuals are symmetrically distributed about zero
(Fig. S6 b) and the range between the 25% and 75% quartile varies from
0.0126 (c2) to 0.027 (c3); the mean interquartile range was 0.02. Taken
together, these results indicate a lower bound of measurement repeatability
on the order of 0.5–1.0% and an outer bound of 2.0–3.0%. The main differ-
ence between short- and long-term measurements of live cells is the shape
change that occurs over the scale of hours. This can cause added variation in
the integrated optical thickness from: 1), small errors in partitioning the cell
boundaries; 2), optical averaging of closely spaced fringes present at the
edge of rounded cells; and 3), a potential change in the value of a, the
mass-to-optical thickness constant, although previous work suggests this
error would be relatively small (3). It is established that 1), a is unaffected
by changes in concentration, even up to the limit of crystallized protein
solutions (9); 2), a reflects the mass interacting with light at a specific loca-
tion (9–12,31) and is, therefore, not affected by how much area the cell
occupies within the field of view as it grows; and 3), the value of a remains
close to 0.0018 over a wide range of materials found in cells (32).
Cell lines and tissue culture
H929 human multiple myeloma cells were maintained at 37
Cin5%CO
2
in RPMI 1640 growth media supplemented with 10% defined fetal bovine
serum (HyClone) and antibiotics. The observation chamber was 4.5 cm in
diameter and 1.5 cm deep with a 2 2 cm silicon substrate placed on top of
a plastic shelf such that the silicon was near the top of the fluid surface. The
imaging cell was completed by a piece of optical glass (BK7 glass, Quartz
Plus, Brookline, NH) separated from the silicon surface by resting on top of
three 600 mm stainless steel beads (Salem Specialty Ball Company, Canton,
CT) to create a uniform thickness sample chamber. Media bubbled with 5%
CO
2
air was continuously flowed through the incubation chamber using
a peristaltic perfusion pump at a rate of 0.5 ml/min. The 530 nm wavelength
LED illumination (Luxeon Star LED, Brantford, Ontario) incident on the
sample chamber had a power of 15 mW spread over a 1.2 mm diameter illu-
mination spot. We have measured cell responses to external stimuli for as
long as 7 h in this configuration, and observed unperturbed cultures for
up to 12 h, although the upper limit of experiment duration has not been
determined.
Drug treatment, cell cycle analysis, and nucleic
acid isolation
H929 cells were seeded in 6-well culture plates at a density of 1 10
6
cells/
well. Before plating cells in the LCI’s observation chamber, either 1 mlof
Biophysical Journal 101(5) 1025–1031
Live Cell Interferometry 1027
Page 3
Tunicamycin (TM) (T7765; Sigma-Aldrich) in dimethyl sulfoxide
(DMSO), or DMSO alone were added to the media at a concentration of
10 mg/ml, DMSO/media (1:1000 dilution). Mass measurements com-
menced 1 h after the cells were plated in the observation chamber to allow
the experimental system to stabilize, i.e., culture acclimation, temperature
stabilization, etc. For cell cycle analysis, cells from each time point were
collected and incubated with a hypotonic DNA- staining buffer containing
propidium iodide and later analyzed by flow cytometry. RNA for each time
point was extracted using the Trizol reagent (Invitrogen).
Reverse transcription-polymerase chain reaction
and quantitative RT-PCR
CDNA was synthesized from 3 mg of total RNA with oligo(dT) primers
using the Superscript III first strand cDNA synthesis kit (Invitrogen). RT-
PCR for XBP1 spliced and unspliced isoforms were performed using Plat-
inum Taq (Invitrogen) at an annealing temperature of 58
C for 25 cycles.
Quantitative RT-PCR for CHOP (DDIT3) mRNA was performed using
the SYBR green real-time PCR kit (Diagenode) and an Applied Biosystems
(Foster City, CA) 7700 sequence detector as described (33). Samples were
analyzed for 36b4 expression as a normalization control. Primer sequences
are available on request.
RESULTS AND DISCUSS ION
Mass accumulation dynamics have not been previously re-
ported on a cell-by-cell basis over long time scales (several
hours) for an entire population of ~100 cells measured
simultaneously. To test the hypothesis that LCI mass
profiling can rapidly determine a response to external cell
stimuli, such as a drug response, we exposed H929 mul tiple
myeloma cells to the drug TM, a protein glycosylation
inhibitor (34), and compared the growth profiles of TM-
treated to untreated control cells by measuring mass contin-
uously over 5 h.
We determined that the initial distribution of H929 cell
masses is approximatel y log-normal, with a range of 200–
700 pg. The majority of cells had mass >200 pg and
<400 pg, although a small fraction (3–6%) are much larger
than average, with masses above 500 pg. Both the treated
and untreated populations exhibited growth, but the mass
accumulation rate was much lower in the treated cells
(Fig. 3). The growth profiles of both populations are clearly
heterogeneous (Fig. 3, a and b), and in both, a minority of
cells exhibited either a vigorous increase in mass (þ15%
growth), or little to no mass accumulation (<5% growth).
The suppression of growth of the treated population appears
within 2 h, and is readily apparent by the fourth hour (Fig. 3,
c and d). Thus, whole population detection and quantifica-
tion of cell drug responses were attained within several
hours of treatment. The variation in growth rates within
the treated and untreated groups (Fig. 3, c and d) at 5 h ap-
proached the magnitude of the variation between treated and
untreated cultures at the same time point. These experiments
were conducted on separate days, with distinct subcultures
taken from a master stock. Therefore, they are biological
not technical replicates, and the difference in behavior likely
reflects biological variation. We used technical replicates on
controlled samples to estimate the measurement error to be
<3% CV (see Methods). Nonetheless, we note that the
differences in normalized final mass (final/initial) between
each treated sample and each untreated sample are
FIGURE 3 Drug response of H929 multiple
myeloma cells profiled by single-cell mass accu-
mulation: Results of LCI longitudinal mass
measurements on populations of H929 multiple
myeloma cells, comparing the mass accumulation
of DMSO-treated controls with TM-treated
(10 mg/ml) cells. Data are taken over 5 h as
described in Methods. The treated cells grow
more slowly than do the controls. Experiments
Nos. 2 and 3 were conducted at 32
C vs. 37
C
for No. 1, which accounts for the slightly lower
overall growth rates observed. The scatter plots
(a and b) depict the growth of individual cells at
5 h versus their initial mass (normalized by initial
mass). Error bars represent 5 2% CV, our estimate
of the measurement error (see Methods). Error bars
apply to all data, but are omitted for the majority of
points in the plot for clarity. In the box plots of
normalized mass versus time (c and d), circles indi-
cate the sample median, and triangles indicate the
95% confidence interval for the median. Solid
boxes indicate limits of the 25 and 75 percentiles,
and whiskers represent two standard deviations
from the mean.
Biophysical Journal 101(5) 1025–1031
1028 Reed et al.
Page 4
statistically significant with p < 0.05 (Fig. 3, c and d). This
supports the conclusion that the LCI is capable of detecting
differences in growth rates between treated and untreated
populations of cells.
At the single cell level, the growth rate of individual cells
is largely independent of cell mass, within experimental
error, for both treated and untreated cells (see Fig. S1). An
exception is treated population Tm1, which showed a statis-
tically significant linear trend toward slower growth in its
larger cell subpopulation. The reason for this difference is
unclear. Interestingl y, the spread in growth rates within
any particular mass fraction cannot be explained entirely
by measurement error, suggesting a biological origin of
this variation as well. This variation, taken as the norm of
the residuals of a linear least squares fit to the growth versus
mass data, ranges from 3.1–5.8% (Fig. S6), whereas we esti-
mate the mass measurement error is <3% CV (see discus-
sion of errors in Methods).
To link the kinetics of mass accumulation with biochem-
ical signaling, we profiled molecular markers with PCR and
conducted a cell cycle analysis on the treated population.
The divergence in growth rates between the treated and
untreated populations occurs synchronously with the up-
regulation of transcription factors CHOP and the spliced
form of XBP1 (XBP1-s), in the treated population (Fig. 4,
a and b). CHOP and XBP1-s activate a host of genes respon-
sible for mitigating the effects of protein misfolding in the
endoplasmic reticulum, through increased production of
molecular chaperones to aid protein folding, and accelerated
degradation of misfolded proteins (the so-called unfolded
protein response, UPR), and the endoplasmic reticulum-
associated protein degradation pathway (35). This is consis-
tent with the known mechanism of TM action (34). Both the
UPR and endoplasmic reticulum-associated protein degra-
dation molecular pathways are emerging targets for thera-
peutic intervention in a wide range of diseases, including
multiple myeloma.
XBP1 is a context-dependent positive or negative regu-
lator of cell growth and differentiation in multiple myeloma
cells (34). The molecular dynamics of its bipolar transcrip-
tional potential is not well understood. In the context of our
experiments, induction of XBP1 mRNA splicing is associ-
ated with slowing mass accumulation, but not cell shrinkage
or apoptosis. This time-resolved, nondestr uctive measure-
ment of cell mass greatly helps interpretations of conflicting
pro- and antiproliferate molecular signals, assayed through
traditional techniques, including immunohistochemistry or
quantitative PCR. Cell cycle data show a rapid reduction
in the G2/M phase population and a corresponding increase
in the G1/G0 population, consistent with cell cycle arrest
(Fig. 4 c). This shift becomes pronounced after 3 h of TM
exposure, leaving 50% of the cells in G1/G0 by the end of
5 h of treatment. This is also consistent with observations
that activation of the UPR pathway leads to cell cycle arrest
(35,36).
We determined the mass range of dividing cells by
observing individual divisions and measuring the mass of
the parent and daughter cells directly. Twenty-eight cell
divisions were observed across all experiments, out of a total
of ~600 cells. The number of divisions was skewed in favor
of the untreated population 18:11. This is consistent with the
observed higher growth rates in that population. The mass at
which a cell divides was tightly regulated, and simi lar in
both treated and untreated populations (Fig. 5 a). The
median mass at division was 515 pg (575 pg), with the
two resulting d aughter cells each having a median mass of
250 pg (540pg). This results enable us to infer via mass
values that individual cells in the population are likely to
be in early-, mid- and late-phases of the cell cycle. Although
the mass fraction for the daughter cells was ~50/50 in most
instances, a minority of cell divisions were highly asym-
metric, with the smaller of the two daughter cells retaining
<45% of the parent’s cell mass (Fig. 5 b). Mass maps of two
cells undergoing asymmetric cell division are shown in
Fig. 5 c.
There are clear advantages of LCI over other establ ished
and emerging methods for single-cell mass measurements.
Unlike hollow cantilever microelectromechanical mass
measurement devices (5,6), which require nonadherent
cells, LCI is equally compatible with adherent or nonadher-
ent cells (Fig. 6). The ability to work with adherent cells is
absolutely critical for probing the relationship between mass
accumulation/distribution and cell-substrate interactions,
FIGURE 4 Molecular profile of H929 response to TM. The divergence in
growth rates between the treated and untreated populations occurs synchro-
nously with the up-regulation of the transcription factor CHOP (a) and the
alternative splicing of transcription factor XBP1 (b) in the treated popula-
tion. CHOP and XBP1-s activate a host of genes responsible for mitigating
the effects of protein misfolding in the endoplasmic reticulum. This is
consistent with the known mechanism of TM action, an inhibitor of protein
glycosylation. (c) Cell cycle data show a rapid reduction in the G2/M phase
population and a corresponding increase in the G1/G0 population, consis-
tent with cell cycle arrest. This shift becomes pronounced after 3 h of treat-
ment, leaving 50% of cells in G1/G0 by the end of 5 h of treatment.
Biophysical Journal 101(5) 1025–1031
Live Cell Interferometry 1029
Page 5
and for assessing epithelial or stromal cell types, which form
the bulk of human malignancies. LCI is also an excellent
approach for linking mass profiling with a whole class of
cell migration, motility, and tissue invasiveness assays
commonly used in drug discovery. The interferometric
microscope permits full optical access to the specimen,
meaning high resolution light micrographs and fluorescent
images are easily obtained. This enables the combined use
of mass profiling and the extensive armamentarium of fluo-
rescent reporter assays used in cell biology, for simultaneous
assessments. Furthermore, LCI demonstrates tracking and
quantification of individual cell masses throughout, and
following, cell division. To our knowledge, this will directly
enable, for the first time, broad spectru m profiling of mass
partitioning in stem cells, for example.
LCI is high-throughput and allows longitudinal measure-
ments of the same cells over time; it is also massively
parallel, enabling hundreds of longitudinal measurements
simultaneously and reducing interexperiment error due to
varying conditions. However, occasionally the conversion
from phase to optical thickness is incorrect by a factor of
negative one wavelength (530 nm), due to the ambiguity
in phase shifts >2p. This situation causes contiguo us
regions with the cell to have an apparent optical thickness
one wavelength less than the true value. This error is easily
detected as a nonphysical discontinuity in optical thickne ss,
and corrected by adding back one wavelength of optical
thickness to the affected pixels. This correction p rocess is
not currently fully automated, although a substantial body
of work addressing this issue exists in the literature (16).
We have measured cell responses to external stimuli for
as long as 7 h, and observed unperturbed cultures up to
12 h. In principle, measureme nts can continue for much
longer durations because the cells remain viable for days
under tightly controlled culture conditions. One limitation,
common to LCI and alternative approaches (5,7,8), is the
time required for the system to stabilize after cells are intro-
duced into the observation chamber, or after media with
a different temperature or density is introduced. In the
present experiments, we have conservatively allowed 1 h
FIGURE 5 Mass dynamics of cell division: Twenty-eight division events
were recorded from the treated and untreated populations in all experiments
(from a total of ~600 cells). (a) We determined the mass range of dividing cells
by observing individual divisions and measuring the mass of the parent and
daughter cells directly. Panel (a) compares the mass distribution of all cells
measured (treated and untreated; dashed line) with the masses of those cells
that divided during the experiment (redbefore the division, blueafterdivision).
(b) Surprisingly, a number of cell divisions were highly asymmetric, with
~55% or more, of the total parent cell mass remaining in the larger of the
two daughter cells. (c) Two examples of highlyasymmetric division are shown
over the 5-h time course. The smaller of the daughter cells in these divisions
(indicated by an asterisk) contained 35% and 40%, respectively, of the parent
cell mass. These division events are indicated by red-solid circles in (b). Error
bars represent 52% CV, our estimate of the measurement error (seeMethods).
FIGURE 6 Live cell interferometer measures
the mass of adherent cells: Frame shows several
mouse fibroblast cells cultured directly on a pol-
ished silicon substrate. The color map indicates
optical thickness measurements with blue being
a low optical thickness relative to background
and red being a high optical thickness. To the left
are mass measurements of the four cells, as indi-
cated, taken every 2 s for 200 min. The smaller
optical thickness of the adherent cells versus non-
adherent cells is easily measured by the LCI.
Biophysical Journal 101(5) 1025–1031
1030 Reed et al.
Page 6
of settling time, although if required this settling time could
be reduced by at least a factor of two.
In summary, high throughput LCI mass profiling is
a sensitive and precise mechanism for quantifying single-
cell, population-based responses to environmental perturba-
tions, such as medically relevant drug responses.
SUPPORTING MATERIAL
Scatter plots showing all cells in treated and untreated data sets, data pre-
senting quantification of measurement error and repeatability, additional
mass histogram data for other cell types, and six figures are available at
http://www.biophysj.org/biophysj/supplemental/S0006-3495(11)00880-0.
We thank K. Niazi and S. Rabizadeh (Abraxis Bioscience and California
NanoSystems Institute, University of California, Los Angeles) for helpful
discussions.
This project was supported by National Institute of General Medical
Sciences (NIGMS) (R21GM074509), a University of California
Discovery/Abraxis Bioscience Biotechnology Award (Bio07-10663) and
by the National Institutes of Health Roadmap for Medical Research Nano-
medicine Initiative (PN2EY018228).
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Biophysical Journal 101(5) 1025–1031
Live Cell Interferometry 1031
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