Single-Cell Tracking of A549 Lung Cancer Cells Exposed to a Marine Toxin Reveals Correlations in Pedigree Tree Profiles

Abstract

Long-term video-based tracking of single A549 lung cancer cells exposed to three different concentrations of the marine toxin yessotoxin (YTX) reveals significant variation in cytotoxicity, and it confirms the potential genotoxic effects of this toxin. Tracking of single cells subject to various toxic exposure, constitutes a conceptually simple approach to elucidate lineage correlations and sub-populations which are masked in cell bulk analyses. The toxic exposure can here be considered as probing a cell population for properties and change which may include long-term adaptation to treatments. Ranking of pedigree trees according to a measure of “size,” provides definition of sub-populations. Following single cells through generations indicates that signaling cascades and experience of mother cells can pass to their descendants. Epigenetic factors and signaling downstream lineages may enhance differences between cells and partly explain observed heterogeneity in a population. Signaling downstream lineages can potentially link a variety of observations of cells making resulting data more suitable for computerized treatment. YTX exposure of A549 cells tends to cause two main visually distinguishable classes of cell death modalities (“apoptotic-like” and “necrotic-like”) with approximately equal frequency. This special property of YTX enables estimation of correlation between cell death modalities for sister cells indicating impact downstream lineages. Hence, cellular responses and adaptation to treatments might be better described in terms of effects on pedigree trees rather than considering cells as independent entities.

Full text

July 2018 | Volume 8 | Article 2601
ORIGINAL RESEARCH
published: 04 July 2018
doi: 10.3389/fonc.2018.00260
Frontiers in Oncology | www.frontiersin.org
Edited by:
Robert Clarke,
Georgetown University,
United States
Reviewed by:
Anton A. Buzdin,
Institute of Bioorganic
Chemistry (RAS), Russia
Hakima Amri,
Georgetown University,
United States
*Correspondence:
Mónica Suárez Korsnes
monica.suarez.korsnes@nmbu.no
Specialty section:
This article was submitted
to Pharmacology of
Anti-Cancer Drugs,
a section of the journal
Frontiers in Oncology
Received: 19March2018
Accepted: 22June2018
Published: 04July2018
Citation:
KorsnesMS and KorsnesR (2018)
Single-Cell Tracking of A549
Lung Cancer Cells Exposed to a
Marine Toxin Reveals Correlations
in Pedigree Tree Proles.
Front. Oncol. 8:260.
doi: 10.3389/fonc.2018.00260
Single-Cell Tracking of A549 Lung
Cancer Cells Exposed to a Marine
Toxin Reveals Correlations
inPedigree Tree Profiles
Mónica Suárez Korsnes1,2,3* and Reinert Korsnes2,3,4,5
1Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway,
2Noma AS, Ås, Norway, 3Korsnes Biocomputing (KoBio), Ås, Norway, 4 Norwegian Defence Research Establishment (FFI),
Kjeller, Norway, 5Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
Long-term video-based tracking of single A549 lung cancer cells exposed to three dif-
ferent concentrations of the marine toxin yessotoxin (YTX) reveals signicant variation in
cytotoxicity, and it conrms the potential genotoxic effects of this toxin. Tracking of single
cells subject to various toxic exposure, constitutes a conceptually simple approach to elu-
cidate lineage correlations and sub-populations which are masked in cell bulk analyses.
The toxic exposure can here be considered as probing a cell population for properties
and change which may include long-term adaptation to treatments. Ranking of pedigree
trees according to a measure of “size,” provides denition of sub-populations. Following
single cells through generations indicates that signaling cascades and experience of
mother cells can pass to their descendants. Epigenetic factors and signaling down-
stream lineages may enhance differences between cells and partly explain observed
heterogeneity in a population. Signaling downstream lineages can potentially link a variety
of observations of cells making resulting data more suitable for computerized treatment.
YTX exposure of A549 cells tends to cause two main visually distinguishable classes
of cell death modalities (“apoptotic-like” and “necrotic-like”) with approximately equal
frequency. This special property of YTX enables estimation of correlation between cell
death modalities for sister cells indicating impact downstream lineages. Hence, cellular
responses and adaptation to treatments might be better described in terms of effects on
pedigree trees rather than considering cells as independent entities.
Keywords: single-cell tracking, pedigree tree profiles, correlation sister cells, yessotoxin, cancer, epigenetic
inheritance
1.INTRODUCTION
Live cell time-lapse microscopy can be a valuable tool for early diagnosis in cancer therapy.
Continuous single-cell tracking over many cell divisions is essential to discover rare cell populations
and heterogeneous cell responses, which can be missed in cell bulk assays. It can therefore provide
the temporal information that is required to identify dierential cell responses and cell fates (1, 2).
e main intention of the present work is to contribute to the development of such tools via a case
study of tracking individual A549 lung cancer cells exposed to the small molecule compound yes-
sotoxin (YTX). is toxin can induce dierent cell death modalities in many cellular systems (3, 4).

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It can activate both caspase-dependent and -independent death
pathways (5–12). It also induces dierent cell death modalities
in A549 cells and which fall into two main morphologically
distinguishable classes with approximately the same frequency
of occurrence. is gives a unique opportunity to observe cor-
relation of cell death modalities for sister cells indicating lineage
downstream signaling. is possible communication channel
may be signicant for enhancement of dierences between line-
ages and adaptation.
YTX can trigger a broad spectrum of cytotoxic responses
(13–21). It can also cause anti-allergic and anti-tumoral eects
(22) and Korsnes and Korsnes (23) demonstrated its ability to
induce genotoxic eects in BC3H1 cells. Several authors have
proposed YTX for biotechnological, pharmaceutical, and thera-
peutic applications due its various cytotoxic and genotoxic eects
(4, 24–27).
is work corroborates the capacity of YTX to induce geno-
toxic eects in A549 lung cancer cells. Treatment of the cells with
three dierent concentrations of this toxin enables to determine
variation in individual cell response and cell fate proles. Cells
exposed to YTX are able to carry out abnormal cell divisions
aecting cell proliferation. Pedigree proles evidence how YTX
exposure notably aects cell division depending on concentra-
tion of the toxin. Asymmetric distributions of the cytoplasm,
multipolar divisions, and nuclear changes also conrm this fact.
ese traits are prominent characteristics of mitotic catastrophe,
which is a regulated oncosuppressive mechanism that impedes
cell proliferation and/or cell survival owing to extensive DNA
damage, problems with the mitotic machinery, and/or failure of
mitotic checkpoints (28). It can result from high levels of DNA
replication stress or it is caused by an aberrant ploidy or by
deregulated chromosome segregation (29, 30).
Single-cell tracking is a developing technology with pro-
spective valuable applications in cancer research and medicine
(2). e present work illustrates examples of distinct statistical
structures in data from such tracking. Extraction of structures in
spatial and temporal observations of single cells can contribute to
the development of automatic search for “signatures” of predic-
tive value in large sets of video data. is can help to understand
cellular processes and also help timely diagnosis and monitoring
for change detection. e approach may be specially relevant
for cancer treatment since populations of cancer cells typically
exhibit signicant variation, and they adapt or become resistant
to drug treatments (2, 31–33). Further development of technology
for single-cell tracking may include introduction of hardware
for new bio-probes to increase the possibilities to monitor intra-
cellular organelles and to identify molecular signaling pathways.
2.MATERIALS AND METHODS
2.1.Toxin
YTX was obtained from the Cawthron Institute (Nelson, New
Zealand). It was dissolved in methanol as a 50µM stock solu-
tion. e stock solution was aer diluted in RPMI medium
(Lonza, Norway), achieving a nal concentration of 2µM YTX
in 0.2% methanol. Treated cells were incubated with 200, 500,
and 1,000nM YTX and control cells were incubated with 0.2%
methanol as vehicle. Control cells and treated cells for Hoechst
labeling were exposed to dierent end points 24, 48, 72, and 96h.
2.2.Cell Culture
A549 cell lines were provided by Dr. Yvonne Andersson and
Dr. Gunhild Mari Mœlandsmo from the Institute of Cancer
Research at the Norwegian Radium Hospital. Cells were cultured
in RPMI 1640 (Lonza, Norway), supplemented with 9% heat
inactivated fetal calf serum (FCS, Bionordika, Norway), 0.02M
Hepes buer 1 M in 0.85% NaCl (Cambrex no 0750, #BE17-
737G) and 10ml 1× Glutamax (100×, Gibco #35050-038), 5ml
in 500ml medium. Cells were maintained at 37°C in a humidied
5% CO2 atmosphere.
2.3.Time-Lapse Video Microscopy
and Single-Cell Tracking
A549 cells were plated onto 96-multiwell black microplates
(Greiner Bio-One GmbH, Germany) for time-lapse imaging.
Cells were imaged into Cytation 5 Cell Imaging Reader (Biotek,
USA), with temperature and gas control set to 37°C and 5% CO2
atmosphere, respectively. Sequential imaging of each well was
taken using 10× objective. e bright and the phase contrast
imaging channel was used for image recording. Two times two
partly overlapping images were stitched together to form images
of appropriate size. A continuous kinetic procedure was chosen
where imaging was carried out with each designated well within
an interval of 6min for a 94h incubation period. Exposed cells
were recorded simultaneously subject to three dierent con-
centrations of YTX 200, 500, and 1,000nM. Control cells were
imaged for 26h in a separate experiment. Technical limitations
of the early version of the recording soware made it dicult to
record all the cells simultaneously because when the density of
the control cells became too high, the exposure settings could be
compromised. See supplementary data providing video from the
recordings.
e single-cell tracking in this work was performed using the
experimental computer program Kobio_Celltrack.1 is system
did facilitate to dene a rectangle in the middle of the video scene
so it initially contained 100 cells to be tracked. Observables from
this approach are as follows:
• Pedigree trees where time tagged nodes represent mitosis or
cell death and edges stand for observed life span for cells.
• Volume estimates of cells observed to round up before divi-
sion. ese estimates are based on measuring diameters of
cells in the state of rounding (short before mitosis).
• Estimates of velocity based on kernel density of positions
(Gaussian kernel with xed bandwidth equal to 15min).
• Visual classication of cell death.
e visual classication of cell death is assumed to be relatively
easy to automatize via image processing.
1 https://www.korsnesbiocomputing.no/ (Accessed: June 30, 2018).

FIGURE 1 | Sample images from time-lapse recording for single-cell tracking. The red frame is large enough to cover initially 100 cells with descendants inside the
imaged area during the following time of recording. The frames are of size 888×484μm2, 858×452μm2, and 840×434μm2 (respectively from left to right). The
cells are exposed to 200, 500, and 1,000nM. The lower row shows the cell population at 60h from the start of exposure. Note the increase of the cell populations
from the start to 60h (largest increase for cells exposed to 200nM). Many cells move out of the initial red frame during the actual period. Scale bar: 100µm.
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e present description of heterogeneity in the study cell line
includes ranking of pedigree trees based of a measure (“size”)
which intuitively represents viability. e common denition of
the size of a graph (or tree) G is simply its number #G of nodes.
However, the present denition of size, M(G), of a pedigree tree
modies this denition as follows:
MG
f
fsc
cG
()
=
() ()
()
∆
∈
∑
1
α
α
τ
(1)
where α is a tuning parameter (here set to 4h−1) for the function
fα(x)=log(αx+e), s(c) represents the observed lifetime of cell c,
and τ=19h (the doubling time for control cells). e is the Euler’s
number. Note that an observed lifetime s(c) counts as 1 if it is
equal to the doubling time τ simply because
11
f
fx
α
α
τ
()
()
= for
x=τ (cf. Equation1). e ordering of pedigree trees according
to this denition of size M(⋅) is only slightly dependent of the
value of α if it is in the range 1–20h−1.
2.4.Nuclear Visualization of Using
Hoechst Labeling
1 × 104 control and YTX-treated cells were xed in 4.0%
paraformaldehyde 7.3 pH for 15 min at room temperature.
Aer xation, cells were washed 3 times with PBS. Cells were
incubated with blocking buer solution (1× PBS in 5% donkey
serum and 0.3% Triton X-100) for 15 min. e xative was
removed and then replaced with prewarmed live cell imaging
solution containing 50 nM LysoTracker red DND-99 (Life
Technologies), and the cells were further incubated for 15min at
37°C. Cells were washed 3 times with Live cell imaging solution
(Termosher, USA). Two drops of NucBlue® Live ReadyProbes®
(Termosher, USA) was added to a 1ml live cell imaging solu-
tion (Termosher, USA). e prepared solution was added to
the cells and incubated for 7min at room temperature. Cells
were then washed two times with live cell imaging solution
(Termosher, USA). Cells were analyzed with a Leica confocal
laser scanner microscope SP5 (Leica Microsystems Wetzlar
GmbH, Wetzlar, Germany).
3.RESULTS
3.1.Revealing Heterogeneity From
Single-Cell Tracking
Visualization of pedigree trees from single-cell tracking can help
to reveal heterogeneity among cells in a population. It supports
detection of possible correlations among mother and daughter
cells and between sister cells and which indicates various forms
of inheritance from mother to daughter cell. e pedigree trees
from the present tracking of A549 cells exposed to yessotoxin,
indicate an information transfer downstream pedigree trees and
which depends on concentration of the toxin. An example of such
inheritance is that sister cells tend to die by similar cell death
modality. Information transfer downstream pedigree trees can
have interest for assessments on how toxins may aect cells over
time.
Figure1 illustrates the organization of the above-mentioned
tracking of A549 cells. e gure shows images of the cells aer
exposure to the three dierent concentrations 200, 500, and
1,000nM of YTX during 1 and 60 h. e red frames are here
precisely large enough to contain 100 cells at start and which
below are called initial populations. Five, four, and one of the cells
exposed to 200, 500, and 1,000nM, respectively, had a descendant
which le the imaged area (these cells and their descendants were
excluded from the statistical treatment below). e supplemen-
tary data include video illustrations of the single-cell tracking as
well as the pedigree trees resulting from it.
Figure 2 shows the size of these three cell populations as
they develop during the observational period of 94h, whereas
Figure3 shows frequency of cell death in the populations during
this period. One can here see that cells start to die mainly aer
40h of YTX exposure. e frequency of mitosis here reduces
aer 50 h for 200 nM exposure and aer 15h for 1,000 nM
treatment.

FIGURE 3 | Frequency of cell division and cell death in three cell populations of initial size 100 individuals. The cells were exposed to YTX at concentrations 200,
500, and 1,000nM. These data result from individual cell tracking. Note that cells start to die at about 40h after exposure except for 500nM where apoptosis-like
cell death starts to appear after 20h. Many cells exposed to 1,000nM enter quiescence after range 30–40h (see also Figures6–8 below).
FIGURE 2 | Development of cell size population initially consisting of 100
individuals. The cells were exposed to three different YTX concentrations
200, 500, and 1,000nM. These data result from tracking the cells which may
divide or die. A short recording of control cells indicate initial exponential
growth with a doubling time 19h. Note that the development of population
size strongly depends on concentration of the toxin which starts to take
effect short after exposure. Subsequent results below show large variations
in the development for subsets of these three populations.
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e development of the population size reects the total eect
from cell division and death which in this work where visually
classied as either apoptosis- or necrosis-like. Figures4 and 5
clarify this classication based on descriptions of macroscopic
morphological alterations as recommended by Galluzzi et al.
(28). e classication facilitates automatic classication via
computer analysis of image time sequences. Apoptosis exhibits
cytoplasmatic shrinkage, plasma membrane blebbing culminat-
ing with the formation of apparently small vesicles (apoptotic
bodies). Cro et al. (34) suggested that destabilization of the
nuclear lamina enables the actomyosin cytoskeleton to tear
the nucleus apart and that this process is required to generate
apoptotic bodies. Necrosis is morphologically characterized
by cytoplasmic granulation, organelle, and/or cellular swelling
(oncosis) terminating with membrane rupture (35).
Classication of cell death has historically been based on mor-
photypes. Its understanding is developing and novel signaling
pathways are still being characterized tending to rely on models
of signal transduction modules involved in initiation, execution,
and propagation of cell death (28). However, the present examples
of strong correlation between cell death modalities in daughter
cells (see Section 3.2 below) indicates a fundamental biological
relevance of the present visual classication.
e above computer-based single-cell tracking provides pedi-
gree trees where each of the initial cell denes the root of a tree
and where events of cell division are time tagged nodes (vertices).
A directed connection (arc) between two nodes represents the
observed life span of a cell. Figures6–8, respectively, show the
10 largest (as sorted according to size), the 10 middle (“median”),
and the 10 smallest pedigree trees for cells exposed to dierent
concentrations of YTX (200, 500, and 1,000nM). Equation 1
here denes the (“size”) ranking of pedigree trees. ese pedigree
trees indicate signicant variation of cellular response to the
YTX exposure. Figures9–10 summarize this variability. Figure9
provides estimates of the size distribution of pedigree trees, and
Figure10 shows the temporal development of number of cells in
the 20% largest and the 20% smallest pedigree trees. Figures6–8
also indicate correlations between cells in the pedigree trees.
Assume a sub-tree where a rst generation daughter cell is the
root node. e size of this sub-tree seems visually positively to
correlate with the size of the corresponding sub-tree for the sister
cell. e pedigree trees tend in general to appear as somehow
symmetric (around the horizontal line through its root). Section
3.2 further elaborates this indication of heritage downstream
pedigree trees.
3.2.Lineage Inheritance and Information
Transfer Downstream Pedigree Trees
Estimates of correlations between morphological features of sister
cells and between mother and daughter cells can contribute to
reveal possible inheritance downstream pedigree trees. Figure11
shows an example where sister cells share morphological features

FIGURE 4 | Example of cell death classied as “apoptosis-like” based of imagery recordings. Apoptotic-like cell death evidences cell shrinkage, dynamic membrane
blebbing until the cell is systematically dismantled into membrane wrapped vesicles (apoptotic bodies). Green arrow points into apoptotic nuclear disintegration
(nuclear extrusion). This morphology can facilitate automatic and objective classication and determination of time for cell death. Scale bar: 20µm.
FIGURE 5 | Example of cell death classied as “necrosis-like” based on imagery recordings. Typical features are cytoplasmic granulation and membrane rupture. The
necrotic cell looks like xed/frozen. This morphology can facilitate automatic and objective classication and determination of time for cell death. Scale bar: 20µm.
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such as vacuoles. It here intuitively looks like the vacuoles of the
mother cell are conserved through cell division and transferred
to the daughter cells. is may be an indication of the capacity
to transfer cell signaling pathways downstream cell division.
Vacuoles need time to form, and here they immediately appear
aer cell division. Hence, it is reasonable to believe that the
daughter cells inherited them directly from the mother. Figure12
more simply illustrates a similar situation. e mother cell here
contains one major observable vacuole which one of the daughter
apparently inherits from her mother. e size and number of
observed vacuoles in the mother and daughter cells are, for both
Figures 11 and 12, consistent with the concept that they are
transferred through cell division.
It is reasonable to believe that inheritance of other organelles
and signal molecules similarly can pass through cell division.
Mothers may initiate cell signaling cascades including cell death
pathways routing to the daughters since they tend to die in the
same way as summarized in Table1.

FIGURE 6 | The 10 largest pedigree trees for cells exposed to 200, 500, and 1,000nM (respectively from left to right). Symbols: “Mit” represents mitosis. Circle here
represents normal rounding during cell division whereas hexagon represents no normal rounding. “Apop” and “Necr,” respectively, represents apoptosis and
necrosis. “LIVE” means the cell still lives at end of recording.
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e shape of pedigree trees (cf. Figures 6 and 8) gives the
impression of correlation between the toxic resistance of sister
cells produced by the rst (root) cell. e trees seem to have a
more symmetric form as compared to hypothetical trees where
cell fate where independent for each cell.
Figure 13 (upper part) includes an illustration of the cor-
relation between the size of subsequent sub-trees of the rst
generation of sister cells in the present observed pedigree trees
(cf. Figures6 and 8). e gure shows estimates of the joint prob-
ability distribution p(x,y) for the size of the observed pedigree
(sub-)trees consisting of the descendants of the rst generation
sister cells (i.e., the sister cells produced aer the rst observed
cell division of the original pedigree trees).
e estimates of the joint distribution p(x,y) of the size of
rst generation sister cell sub-trees are kernel density estimates
of p(x,y) based on joint observations (xi, yi) of the size xi and
yi, i= 1,2,…,N of sub-trees for N tuples of sister cells. It is here
no preference between sister cells so the probability distribu-
tion p(x,y) is assumed to be symmetric (i.e., p(x,y)=p(y,x)).
e observations are therefore swapped to impose symmetry in
the way that if (xi, yi) represents an observation, then also (yi, xi)
is also part of the set of (joint) observations.
Figure 13 shows a rich structure of the joint distributions
p(x,y) for 200nM exposure. e distribution seems to reect three
main groups of pedigree trees reecting dierent toxic resistance.
ese groups also seem to match main parts of the distribution
for 500 and 1,000nM exposure. Figure13 also shows correlations
between volume of mother and daughter cells and maximum
velocity of sister cells (cf. Section 2.3). A general impression from
Figure13 is that the lowest concentration of exposure tend to give
the highest correlations between sister cells and between mother
and daughter cells. Tab le 1 also shows correlation between the
type of cell death of sister cells insituations where both sisters are
observed to die. Figure14 shows observed life span for these cells.

FIGURE 7 | Pedigree tree number 46–55 (“median”) for cells exposed to 200, 500, and 1,000nM (respectively from left to right).
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e classication in apoptotic- and necrotic-like cell death
are here as above (cf. Figures4 and 5). e table shows that cell
death tend to appear as either apoptotic or necrotic for both sister
cells exposed to YTX at concentrations 200, 500, and 1,000nM.
Simple statistical hypothesis tests show (for example, by simula-
tion) that the two cell death modalities are clearly correlated. e
following test statistic can here serve for formal hypothesis testing
independently for each concentration of YTX:
z
N
N
different
total
=
(2)
where Ntotal denotes the number of combined observations of cell
death type of two sister cells (“Sister 1” and “Sister 2”), and Ndierent
denotes the subset of observations where cell death modalities
are dierent. Note that there is consistence between the present
observations of sister cell death for the three dierent concentra-
tions of YTX.
3.3.Special Sign of Genotoxicity
A549 cells exposed to YTX oen exhibit various types of abnor-
malities during mitosis, delay in mitotic rounding, abnormal
midbody structure which is usually thick or very elongated
between diving cells, delay in resolution of chromatin bridges
which may contribute to failure in cytokinesis (cf. Figures 12,
15 and 16). Failure in cytokinesis can lead to multipolar mitosis
and asymmetric cell divisions (29, 37–40). YTX exposure tends
to make A549 cells to delay a second round of mitosis. Korsnes
and Korsnes (23) showed a similar eect on BC3H1 cells and
which indicates genotoxicity. Figure17 shows the distribution of
observed life span of cells aer the rst and second cell division.
Note here that only a part of the population tend to delay the
second round of division or die. is means that some cells seem
to resist the toxin treatment much better than others. Figure17
(lower part) also shows that the frequency of abnormal cell
rounding increases downstream pedigree trees (and later in
time). is additionally supports the idea that YTX is genotoxic
for A549 cells. Results from Hoechst labeling (Figure 18) also
support it. Such labeling reveals nuclear shrinkage and nuclear
envelop deformation adopting a lobulated form. ese are typical
signs of genotoxic eects.
4.DISCUSSION
Tracking of single A549 cells exposed to YTX reveals hetero-
geneity and lineage correlations in cell response depending on
the concentration of the toxin. Korsnes (4) brought up the
possibility to use YTX as a tool to induce dierent cell death

FIGURE 9 | Size distributions for pedigree trees from A549 cells exposed to YTX at concentrations 200, 500, and 1,000nM. These estimates are kernel densities
for the size of three sets of 100 pedigree trees (see text). The kernel bandwidths are here according to the rule of thumb of Silverman (36). Note overlap of size for
the three distributions.
FIGURE 8 | The 10 smallest pedigree trees for cells exposed to YTX at concentrations 200, 500, and 1,000nM (respectively from left to right).
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modalities, and she demonstrated this potential exposing
BC3H1 cells to 100 nM YTX. e present selection of YTX
concentrations (200, 500, and 1,000nM) cause induction of
“apoptosis-like” and “necrosis-like” cell death to occur with
about the same frequency for A549 cells. ese concentrations
did also practically help to reveal how pedigree tress can depend
on concentration. e unique capacity of YTX to trigger dier-
ent cell death modalities at approximately the same frequency
(for the present range of concentrations), enables to correlate
these modalities for sister cells. e observed tendency of sister

FIGURE 10 | Development of number of cells from the 20% smallest and 20% largest pedigree trees in a cell population initially consisting of 100 individuals.
The cells were exposed to YTX at concentrations 200, 500, and 1,000nM. These data result from tracking the initial cells and their descendants.
FIGURE 11 | Subsequential images showing vacuole inheritance. Vacuoles (red arrow) pass from mother cell to her daughters through cell division. Scale bar: 20µm.
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TABLE 1 | Number of observations of apoptotic- or necrotic-like cell death for
sister cells exposed to YTX at concentrations 200, 500, and 1,000nM.
Apoptosis: A
Necrosis: N
Sister 2
200nM 500nM 1,000nM
ANANAN
Sister 1 A12 1 21 5 16 9
N1 11 3 19 6 22
This result is from tracking three populations of 100 initial cells, respectively, exposed to
YTX at these three concentrations during 94h. “Sister 1” here denotes the sister with
the longest life span.
FIGURE 12 | Daughter cell inherits a vacuole from its mother (red arrow). Green arrow points on delay abscission in cells with persistent chromatin in the
inter-cellular bridge. Scale bar: 20µm.
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cells to die the same way, indicates a general “channel” or capac-
ity for downstream signaling and adaptation to stress. is can
be a mechanism for accumulation of epigenetic memory. Such
accumulation may partly explain the observed heterogeneity
among the cells. However, genetic variation can also contribute
to it.
A further development of the present study may include
comparison of populations with slightly dierent genetic
composition. e comparison may reveal to which extent such
dierences can aect the statistical properties of pedigree trees.
Long-term cultivation of cells under slightly unlike conditions is
a conceptually simple way to produce dierent populations for
such experiments.
e possibility to test hypotheses against observations gene-
rally makes them more interesting than otherwise. Single-cell
DNA sequencing may in dierent ways provide testing of the
conjecture that epigenetic factors are signicant to explain the
observed heterogeneity among A549 cells exposed to YTX.
Assume exposed cells are tracked for a period long enough to
form pedigree trees of various sizes. en the tracking may be
stopped for subsequent sequencing in a way so single cells still can
be identied as part of a pedigree tree. is enables to correlate
the DNA of single cells with their life history. A complementary
approach using DNA sequencing is to make “twin studies” of sis-
ter cells or make analyses of subsequent pedigree trees for them.
e results above (Section 3.2) show that sister cells are correlated
with respect to how that die or the viability of their descendants
(size of the pedigree tree formed by their descendants). Assume
one manage to retrieve many single cells for DNA sequencing,
but still let many of then continue undisturbed. Also assume
one manage to conserve the identity of all cells (those retrieved
and those not retrieved). en one may know (statistically or
partly) the potential fate of individual sequenced cells as if they
are still alive. is approach, however, technically challenging,
could contribute to distinguish between a hypothesis that DNA
mostly counts for heterogeneity as opposed to the possibility that
observed variation depends on epigenetic mechanisms.
Single-cell tracking directly indicates that yessotoxin is geno-
toxic for A549 cells. Korsnes and Korsnes (23) showed similar

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eects for BC3H1 cells. A sign of genotoxicity for cells exposed to
a toxin is that they tend to exhibit aberrant mitosis and multipolar
divisions (29, 41–44).
Dividing cells normally adopt a short-term spherical shape
known as mitotic cell rounding (45). is behavior is common
for most eukaryotic cells ensuring that all chromosomes are
timely captured by bringing them close together with spindle
microtubules (45). Hence, proper mitotic rounding is considered
to enable ecient and stable bipolar spindle assembly for precise
and timely mitotic progression (46).
YTX treatment can aect mitotic rounding of A549 cells. e
cells can fail to reach proper spherical rounding or the rounding
takes long time. is may disrupt spindle assembly altering chro-
mosome capture during mitotic progression which may enable
asymmetric cell divisions as shown in Figures15 and 16.
e nuclear pore complex and components of the nuclear
envelop can have dierent active roles in mitotic events (47).
Deregulated division of cancer cells are prone to defects in
both the morphology and proteins of the nuclear envelop (48).
Its possible structural changes such as low levels of lamins
FIGURE 13 | Top row: joint distribution of size of subsequent sub-trees for sister cells. The surfaces are smoothed version of normalized impulses at positions given
by associated values from tuples of sister cells. Symmetry is forced by switching values for sisters (articially doubling the number of observations). Middle row: joint
distribution of volume of mother and daughter cell. Bottom row: observed maximum velocity of sister cells.

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FIGURE 14 | Comparison of life span for sister cells observed to be born and die bye necrosis and/or apoptosis during the observation period of about 94h. The
cells were exposed to YTX at concentrations 200, 500, and 1,000nM. “Sister 1” denotes the one with the longest life span of two related sisters. Note that there are
more mixed cell death modalities (green) for sister cells exposed to 1,000nM YTX as compared exposure with the lower concentrations. Necrosis tends partly to
appear later than apoptosis for exposure by 500nM.
FIGURE 15 | Time-lapse images of mitosis in control and exposed cells treated with YTX. Control cells exhibit normal mitotic rounding and the cells adopt a
complete spherical form (green arrow) indispensable for timely mitotic progression. Exposed cells show failure in cell rounding (red arrow) which may induce
defects in spindle assembly, pole splitting, and delay in mitotic progression. Scale bar: 20µm.
can result in lobulated nucleus (49). YTX treatment tends
to make the nuclear envelop to adopt lobulated forms. is
probably results from alterations in lamin levels or other key
structural nuclear envelop proteins. Lamins undergo dramatic
remodeling during cell division (50), and errors here can
contribute to various alterations including aneuploidy, mitotic
spindle assembly, and other profound aberrations in mitosis
(51, 52).
Section 3.2 provides rationales for the idea that signaling pro-
teins can transfer directly from a mother cell to her daughters where
they play a role in their subsequent fate. e idea to use time-lapse
studies to reveal such information transfer downstream cellular
lineages is not new. Both Arora etal. (53) and Barr etal. (54) point
out the possibility of using time-lapse studies to link information
about how endogenous DNA replication stress in mother cells
can pass through daughter cells and later generations.

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Transfer of information downstream lineages may change cell
populations and facilitate accumulation of information including
adaptation to toxins. Adaptation can here be looked at as a simple
form of learning. An open question is how developed or complex
this potential learning may be and if there are evolutionary
conserved “channels” for signaling downstream pedigree trees
to provide input for “decisions.” e present results indicate
that 1,000nM YTX exposure reduces correlations between cells
downstream pedigree trees as compared to the exposure at lower
concentrations (200 and 500nM). One may therefore expect that
exposure at higher concentrations reduces the ability to adapt to
toxic stress.
Cell lineages may link observations from dierent cells and
help to provide prognoses from combined analyses. Parameters
derived from one event of mitosis may statistically correlate
to later events, but without knowing about possible related or
“linked” events, an event may appear as “random.” Similarly, two
events of cell death may appear (unconditionally) independent.
However, with the possible information that the cells are sisters,
they may be considered dependent (cf. Tab l e  1 ). e information
FIGURE 16 | Three examples of asymmetric cell division for A549 cells exposed to YTX at concentrations 200, 500, and 1,000nM (respectively from top to
bottom). Blue arrow illustrates multipolar mitosis and yellow arrow shows a defective mitotic spindle morphology which may affect chromosome alignment. Scale
bar: 20µm.

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that cells are sisters, provide additional information useful
to predict their possible cell fate. Two events may, in terms of
statistical theory, be independent, whereas they are conditionally
highly related. Kinship relations may therefore serve to link
large amounts of observations of cells to nd information of
interest.
Data from tracking single cells subject to various treatments
can be stored in large combined databases to make it available for
computerized data mining (such as application of “Big Data”).
e treatments of cells may function as probing them for informa-
tion. Some treatments may also provide information on potential
bioactivity of toxins (bioprospecting). Computerized search in
data from single-cell tracking can presumably bring knowledge
of medical relevance beyond the reach via direct single human
assessments. It may produce prognoses and diagnoses best tting
to, for example, clinical observations. e approach may help to
nd connections between invitro, invivo, and clinical data and
in this way bringing extra value from, for example, experiments
on cell lines.
Smart computer systems can in principle nd structures in data
and optimize denitions to improve predictive power. Structures
in lineage data can provide inspiration and also be directly
relevant for establishing computerized treatment of data from
singe-cell tracking. e denition of, for example, the “size” of a
pedigree tree (Equation1) is here only meant to be a pragmatic
attempt to reect viability according to a simple linear ordering.
A computer system may optimize this denition to uncover
structures of biological or medical relevance. e present linear
ordering of pedigree trees may generalize to relations involving
many parameters (not only one as above). e general philosophy
here is that preliminary semi-optimal attempts can contribute to
nd models of more predictive power.
Collections of pedigree trees from cells subject to dierent
treatments can provide quantication of diversity, detection of
change in populations, and emergence of sub-populations as
well as possible signaling downstream pedigree trees. Large sets
of pedigree trees can facilitate automatic search for signatures
to nd relations and knowledge otherwise not available from
limited experiments. Probabilistic descriptions of pedigree trees
can give opportunities to track cells in more eective multi-target
based ways as compared to a naive approach following singe cells
independently one at a time. It can help to resolve ambiguities
in cell tracking and in this way facilitate ecient sampling and
error detection.
FIGURE 17 | Upper part: distribution of observed life span for cells exposed to YTX at concentrations 200, 500, and 1,000nM after rst and second division. Lower
part: fraction of mitosis events without proper cellular rounding. Note that this fraction roughly seems to increase for each cell cycle. A cell is here formally dened to
“round up” if the radius of the maximum disk included in/inside (the image of) the cell and the radius of minimum disk including/covering the cell, differ less than 10%.

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FIGURE 18 | Hoechst labeling of A549 cells showing nuclear envelope defects after being exposed to 200, 500, and 1,000nM YTX for 24, 48, 72, and 96h. First
column: Hoechst labeling for control cells showing normal nuclei with normal nuclear envelopes. Second, third, and fourth columns show cells exposed to 200, 500,
and 1,000nM, respectively. Note deformed nuclei with lobulated nuclear envelopes in YTX-treated cells. Scale bar: 25µm.
Cancer cells may progressively accumulate genetic mutations
derived from clonal evolution. However, only a clonal minority
may be responsible for cancer progression (55–57). Epigenetic
changes also contribute to cellular heterogeneity because they
promote changes in gene functions/interactions and propagate
heritable changes in the phenotype without aecting the DNA
sequence (56). ese changes can maintain the phenotype into
the adulthood and for subsequent generations (58, 59).
A type of epigenetic memory which can help adaptation to
stress is connected to the nuclear pore complex (NPC) which
is a large molecular portal penetrating the nuclear envelop to
facilitate nuclear-cytoplasmatic tracking (60). Guan etal. (61)
demonstrated that many yeast genes induced by oxidative stress
are activated more rapidly in cells that have previously experi-
enced salt stress. is eect persists for up to four generations
aer the initial stress.
e signicance of epigenetic inheritance of cellular pheno-
type during cell divisions has remained underestimated (62). e
stability of the cellular mRNA and proteins confers the capacity
to a cell to conserve a stable gene expression level and transmit it
over multiple generations even if transcription and translation are
highly uctuating. In addition, reducing short-term uctuations
through high stability of the molecules can be considered as a
simple way of transcription noise reduction at a low energy cost.
Indeed, it takes less energy for the cell to maintain the constant
level of a protein by not degrading the molecules already present
than continuously re-synthesizing them (63).
Several authors have commented on the signicance of
“non-genetic” information transfer from mother to daughter
cells. Memory mechanisms of gene transcription regulation
may explain observed transmission of phenotypes downstream
lineages (64). However, these mechanisms are also blurred by

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noise (63) and which may generate variability between cellular
lineages.
Genetic mutations and thermal “noise” during protein synthe-
sis may explain variability among treated cells. However, signal
transfer downstream lineages (memory) may amplify dierences
between cells. It is therefore reasonable to believe that if there was
a “reset” at each cell division in a clonal population, then there
would be less variability than presently observed. e idea that
various signal molecules can pass through the mitosis process
has general interest since such transfer from mother to daughter
cells can probably have an evolutionary advantage in avoiding the
cost of adaptation. A tendency of “listen to your mother” can, for
example, save energy of signaling and sensing as compared to a
full “reset” during mitosis.
e cost saving by avoiding “reset” at cell division may be in
terms of energy, risk of failures, and restrictions for dierent cel-
lular processes. A mother cell might signalize to her daughters not
to divide to avoid high transmission of replication errors, however,
those cells may still have a function in the organism. Unresolved
replication stress inherited from a mother cell may cause her
daughters enter quiescence (53). Parental experiences from envi-
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A main concern in cancer treatment is development of drug
resistance. Random genetic mutations may occasionally make
some cancer cells viable even under treatment and which sub-
sequentially initiate a resistant sub-population. Another way
to drug resistance is that mother cells transfer information to
their daughters such as damaged proteins or low-level of DNA
damage and which sometimes can increase robustness in a cell
population leading to cell proliferation (53, 66). Signals from a
mother cell may, for example, help her daughters to save cost
establishing counter-measures to toxic exposure such as DNA
repair mechanisms. Cell tracking experiments may in principle
help to distinguish between these two hypotheses. Genetic
mutations are random events whereas adaptation via signaling
downstream pedigree trees is to a larger extent deterministic and
would presumably take place in several pedigree trees pretty close
in time as opposed to random mutations which would appear as
rare singular events.
Single-cell tracking analysis is therefore a powerful approach
that provide more precise analysis of rare sub-populations masked
in cancer cell populations. Transfer of information between single
cells can take place via epigenetic changes and these changes are
conserved through descendants. Data analysis derived from
single-cell tracking allow elaborating pedigree tree proles and
discover that those proles may vary signicantly applying the
same concentration of toxin treatment. is information may
be relevant for treatment of cancer drug resistance which is a
common characteristic acquired for many types of cancer. New
technology for high-resolution observations of molecular signal-
ing pathways is a prospective further step in this development of
methods to control cancer.
AUTHOR CONTRIBUTIONS
MK conceived the study and conducted the laboratory experi-
ments, RK made the computer programming; both authors
analyzed the results and wrote the manuscript.
FUNDING
is study was supported by Olav Raagholt and Gerd Meidel
Raagholts legacy and Eckbos legacy. e work was also supported
by internal funding at the Norwegian University of Life Sciences
(NMBU).

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e reviewer HA and the handling Editor declared their shared aliation.
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