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© 2020. Published by The Company of Biologists Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction
in any medium provided that the original work is properly attributed.
Automated 3D light-sheet screening with high spatiotemporal
resolution reveals mitotic phenotypes
Björn Eismann1,2†, Teresa G Krieger1,2,3†, Jürgen Beneke2,5, Ruben Bulkescher2,5, Lukas
Adam1,2, Holger Erfle2,5, Carl Herrmann1,6, Roland Eils1,2,3,4,7 *, Christian Conrad1,2,7 *
1Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ),
Heidelberg, Germany
2Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant),
University of Heidelberg, Heidelberg, Germany
3Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin,
Berlin, Germany
4Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and
Molecular Biotechnology (IPMB) Heidelberg University, Heidelberg, Germany
5Advanced Biological Screening Facility Center for Quantitative Analysis of Molecular and
Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
6Health Data Science Unit, Medical Faculty University Heidelberg and BioQuant, Heidelberg,
Germany
7Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany
† These authors contributed equally
* Corresponding authors: [email protected], [email protected]
Key words
cell cycle; high-content screening; light-sheet microscopy
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JCS Advance Online Article. Posted on 15 April 2020
Summary statement
A workflow using light-sheet microscopy to evaluate morphologic phenotypes in 3D cell
cultures enables high-throughput studies of dynamic biological processes, target gene
characterization and drug target candidate screens in vitro.
Abstract
3D cell cultures enable the in vitro study of dynamic biological processes such as the cell
cycle, but their use in high-throughput screens remains impractical with conventional
fluorescent microscopy. Here, we present a screening workflow for the automated evaluation
of mitotic phenotypes in 3D cell cultures by light-sheet microscopy. After sample preparation
by a liquid handling robot, cell spheroids are imaged for 24 hours in toto with a dual-view
inverted selective plane illumination microscope (diSPIM) with a much improved signal-to-
noise ratio, higher imaging speed, isotropic resolution and reduced light exposure compared
to a spinning disc confocal microscope. A dedicated high-content image processing pipeline
implements convolutional neural network based phenotype classification. We illustrate the
potential of our approach by siRNA knock-down and epigenetic modification of 28 mitotic
target genes for assessing their phenotypic role in mitosis. By rendering light-sheet
microscopy operational for high-throughput screening applications, this workflow enables
target gene characterization or drug candidate evaluation in tissue-like 3D cell culture models.
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Introduction
The cell cycle with its highly conserved and tightly regulated phases plays a key role in
cancer development and progression. Cell cycle alterations are a hallmark of human tumors,
and many cell cycle proteins have oncogenic properties (Otto and Sicinski, 2017).
Pharmacological or genetic modulation of mitotic oncogene expression is therefore a highly
promising treatment approach.
To study dynamic biological processes such as the cell cycle in vitro, 3D cell cultures provide
a niche microenvironment that replicates the in vivo tissue more closely than traditional 2D
methods (Pampaloni et al., 2007). Cellular and subcellular morphologies can thus be tracked
in a physiologically relevant context, allowing the characterization of therapeutic target gene
function and the evaluation of molecular perturbations. However, live fluorescent imaging of
3D tissue-like cell cultures with conventional laser scanning microscopes is problematic due
to insufficient acquisition speed, low resolution in the Z direction, excessive light scattering
within the tissue and high phototoxicity (Ntziachristos, 2010).
To overcome these challenges, recent advances in selective plane illumination microscopy
(SPIM) or light-sheet microscopy provide imaging capabilities with increased acquisition
speed, excellent optical sectioning, and high signal to noise ratio (Kumar et al., 2014; Power
and Huisken, 2017; Wu et al., 2013). Phototoxicity is reduced by separating excitation and
detection axes, and exciting fluorophores in a single thin layer with a scanning Gaussian
beam. SPIM thus enables the evaluation of phenotypes at the subcellular level in whole-
spheroid or whole-organoid 3D cultures, with sufficient temporal resolution to visualize fast
processes such as mitosis (Pampaloni et al., 2015; Strnad et al., 2016).
While these features in principle make SPIM microscopes ideally suited to high-throughput or
high-content screens, their distinct geometry and the large volumes of data generated pose
new challenges for sample preparation as well as data processing and analysis (Preibisch et
al., 2014; Schmied et al., 2016). Automated phenotype evaluation usually requires the
delineation of imaged structures (segmentation) and their clustering into functional groups
(classification) (Boutros et al., 2015). Classical machine learning methods such as random
forests (RF) employ a user-defined set of features to categorise structured input data
(Breiman, 2001; Ho, 1995). More recently, deep artificial neuronal networks such as
convolutional neuronal networks (CNN) have emerged as a promising alternative (Krizhevsky
et al., 2012). They can use unprocessed images as input and achieve image classification
without the need for predefined features, often resulting in superior performance
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(Angermueller et al., 2016; Godinez et al., 2017; Pelt and Sethian, 2017; Van Valen et al.,
2016), but require large annotated training data sets which limits their usability (Sadanandan
et al., 2017).
Here, we developed a high-throughput screening workflow for the automated evaluation of
mitotic phenotypes in 3D cultures imaged by light-sheet microscopy, from sample preparation
to quantitative phenotype description. By using commercially available technology, this
workflow is reproducible and easily adaptable to different cell culture models or molecular
perturbations. A liquid-handling robot executes automated sample perturbation and mounting.
Light-sheet imaging is performed with a dual-view inverted selective plane illumintation
microscope (diSPIM), a commercially available upright light-sheet system enabling high-
throughput imaging of standard 3D cell cultures at isotropic resolution. A dedicated high-
throughput image processing pipeline optimized for the diSPIM acquisition geometry
combines convolutional neural network-based cell cycle phase detection with random forest-
based classification to quantify phenotypic traits. Using this approach, we detect mitotic
phenotypes in 3D cell culture models following modulation of gene expression by siRNA
knock-down or epigenetic modification. Our fully automated workflow thus adapts light-sheet
microscopy for applications in high-throughput screening in 3D cell culture models.
Results
Light-sheet imaging screen for high-content mitotic phenotype quantification
To evaluate the applicability of SPIM for high-throughput screening of mitotic phenotypes in
3D cell culture, we used an MCF10A breast epithelial cell line (Soule et al., 1990) stably
expressing H2B-GFP to label DNA throughout the cell cycle. MCF10A cells provide an
established and widely used model for benign breast tumors, with single MCF10A cells
developing into multicellular 3D spheroids over the course of several days when seeded into
laminin-rich hydrogel (Matrigel) (Debnath et al., 2003). We selected 28 mitotic target genes
of interest for a high-throughput screen based on reported mitotic roles and a strong
correlation (Pearson correlation > 0.5) or anti-correlation (Pearson correlation < -0.5) of gene
expression with altered methylation levels at one or multiple CpGs in the promoter or a
distant regulatory genomic region, respectively (Methods and Table S1). Target gene knock-
down by siRNA transfection enabled us to analyze the effects of altered expression of these
cancer-related genes in MCF10A cells. For the siRNA screen, two different siRNA were
chosen per gene of interest, and MCF10A H2B-GFP cells were transfected by solid-phase
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reverse transfection (Erfle et al., 2008). INCENP was used as a positive knock-down control
due to its known severe effects on mitosis (Cai et al., 2018), while non-coding siRNA was
used as negative control.
To achieve automated sample preparation, we developed a protocol for a liquid handling
robot, which mixes the pretreated cells with Matrigel and mounts them in small Matrigel
spots in a defined grid on the imaging plate (Fig. 1A). This approach not only minimized the
Matrigel volume to 0.2 µl per spot, reducing cost, but also ensured reliable positioning of
samples with minimal pipetting variations or human errors.
After five days of cell culture at standard conditions, 3D spheroids were imaged with a
diSPIM system for 24 hours at five-minute time intervals (Fig. 1B). This choice of parameters
accommodates the long cell cycle time of MCF10A cells (~ 21 hours (Araujo et al., 2016))
and enables single-cell tracking and the identification of subtle changes in the timing or
morphology of nuclei undergoing mitosis.
Our imaging setup successfully realized the superior imaging capabilities of light-sheet
microscopy over conventional confocal microscopes, including an improved signal-to-noise
ratio, higher imaging speed, isotropic resolution and reduced light exposure compared to a
spinning disc confocal microscope (Table S2). Reductions in image quality due to in-depth
light scattering were negligible after dual-view image fusion (Fig. S1A). Due to the high
temporal and spatial resolution, we were thus able to evaluate global, cellular and subcellular
properties of mitotic gene knock-down phenotypes in live 3D spheroids (Fig. S1B,C,D).
We acquired live long-term image data for three spheroids per siRNA, amounting to 74
terabytes of raw data for a total of 228 spheroids. To address the challenge of data processing
(Fig. 1C), we developed a high-throughput image processing tool called ‘hSPIM’, specifically
tailored to the diSPIM geometry and acquisition properties. This pipeline allows for fast
image fusion, deconvolution and data depth reduction of the raw diSPIM image data, based
on a registration matrix and point spread function (PSF) detected from reference fluorescent
beads. Sample background signal was reduced in our workflow by physically separating
beads from spheroid samples; the registration matrix and PSF determined by imaging beads in
Matrigel at a defined position was transferred to all acquired dual view stacks for registration
and deconvolution. Additionally, we provide with ‘hSPIM’ basic single nuclei segmentation
and textural feature detection. With most calculations executed in parallel on a high-
performance GPU, we were able to process the raw data of one acquisition file within 8.23
seconds, reducing data size from 1.1 GB to 230 MB per position and time point
(approximately 77 MB image data, 153 MB nuclei mask and 14 kB Haralick’s texture
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features) while significantly improving XYZ isotropic resolution and signal-to-noise ratio
(Fig. S1).
The processed image data was subsequently analyzed in a KNIME (Berthold et al., 2009)
workflow. Key properties of individual spheroid development were quantified over time,
including global descriptors of shape, volume and growth (e.g. total nuclei number, spheroid
volume) as well as single nuclei specific traits such as cell cycle phase and position within the
spheroid (Table S3). To reliably identify the cell cycle phase of each nucleus, we compared
the accuracy and performance of a random forest based approach with a VGG-based
convolutional neuronal network classifier (Simonyan and Zisserman, 2015) applied to the
same manually annotated nuclei set (Fig. S2). The random forest classifier relied on
Haralick’s texture features (Haralick et al., 1973) calculated by the ‘hSPIM’ processing
pipeline and detected mitotic phase with 83% accuracy, whereas the CNN classified raw input
image slices directly with 96% accuracy into the four key cell cycle stage (prophase,
anaphase, metaphase, interphase) and was therefore chosen for all analyses.
Detection of mitotic phenotypes in siRNA-treated MCF10A spheroids
Accurate cell cycle phase detection by CNN classification (Fig. 2A) and the high temporal
resolution of the diSPIM screen allowed us to track single cells through the different phases
of the cell cycle (Fig. 2B,C). In non-transfected control samples, 94.7% of all nuclei
throughout the time lapse were classified as interphase, 0.9% as prophase, 1.2% as metaphase
and 3.2% as anaphase (Fig. 2D). As the high isotropic spatial resolution allowed us to
determine nuclei and spheroid volumes at different stages, we also confirmed that prophase
nuclei were on average the largest, followed by inter- and metaphase nuclei. While it has been
suggested that nucleus position within the tissue influences cell cycle fate (Pettet et al., 2001),
we registered a similar radial distribution of cells in all cell cycle phases within MCF10A
spheroids at day six of clonal development (Fig. 2E).
In siRNA transfected samples, we observed a wide range of mitotic phenotype alterations. An
over-representation of different cell cycle phases compared to control MCF10A spheroids
indicated cell cycle arrest; INCENP and AURKA knock-down spheroids, among others,
showed more cells in prophase (Fig. 2F), while MYC and ATOH8 knock-down resulted in
more anaphase cells. PLK1 knock-down nuclei displayed an increase in all mitotic classes,
suggesting an elongated cell cycle (Fig. 2F). Apoptotic cells frequently led to the assignment
of improper mitotic phase transitions (such as prophase to interphase) in PLK1, EME1 and
CEP85 knock-down spheroids. Defects in spheroid growth were identified in INCENP,
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AURKA and PLK1 knock-down spheroids, among others, whereas we did not observe
abnormal positioning of cells in individual cell cycle phases (Fig. 2G).
Hierarchical clustering of all mitotic phenotype quantifications (Table S3) distinguished five
major phenotypic groups (Fig. 3). Cluster 1 comprised spheroids with a high growth rate
closely resembling the non-coding siRNA control. Cluster 2 mostly contained samples with
increased nuclear volumes during prophase and a higher proportion of cells in this phase,
indicating prophase arrest and formation of macronuclei with increased DNA content. A
higher volume growth rate was detected in spheroids in cluster 3, with some knock-down
target genes (LMNB2, F11R, LHFP) also resulting in larger nuclei during anaphase. Cluster 4
spheroids showed strong phenotypes with reduced spheroid volume growth and a low total
number of cell cycle transitions, indicating diminished mitotic activity. Finally, cluster 5
spheroids showed aberrant phenotypes in several features, describing grave cellular and
mitotic defects.
To evaluate the performance of the diSPIM screening workflow and analysis pipeline, we
compared detected phenotypes to the MitoCheck database assembled from imaging the first
two to four cell divisions of HeLa H2B-GFP cells upon siRNA target gene knock-down (Cai
et al., 2018). Notably, despite the use of different cell lines, all of the strong phenotypes
identified in our study were also described in the MitoCheck screen, including reduced
spheroid growth in EME1 and ATOH8 knock-down cultures, elongated cell cycle phases in
ESYT2 and PLK1 knock-down cells, as well as cell cycle arrest and increased apoptosis in
INCENP, MAP7, DSE and PRC1 knock-down cells. Minor phenotypes such as macronuclei
formation in some spheroids with CEP85 or MEIS2 knock-down and interphase arrest
induced by MYC siRNA transfection were not detected by the MitoCheck screen.
Detection of mitotic phenotypes after epigenetic perturbation
To further demonstrate the utility of our workflow, we used a novel molecular tool based on
the CRISPR/Cas system (Cong et al., 2013; Jinek et al., 2012) to target regulatory epigenetic
elements of the selected mitotic genes. Fusion proteins of deactivated Cas9 with no
endonuclease activity (dCas9) with the effector domain of methylome-modifying proteins
(dCas9-ED) have been shown to alter the epigenome at a targeted genomic location defined
by an appropriate sgRNA (Gilbert et al., 2013; Pulecio et al., 2017). We designed fusion
proteins of dCas9 and the effector domain of either DNMT3a methyltransferase to achieve
CpG methylation or TET1 for demethylation (Fig. S3). A dCas9 without added effector
domain was used as a control physically blocking binding sites for regulatory factors. As
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MCF10A cells are highly resistant to plasmid transfection, we moved to human embryonic
kidney 293 (HEK293) cells, which also develop into multicellular spheroids when seeded in
Matrigel, and generated cell lines stably expressing the different constructs.
In a 2D pre-screen, we detected overall low effectivity of the dCas9-ED fusion proteins across
our set of target genes (Methods and Fig. S4), but identified the target gene RGMA as robustly
showing a mitotic phenotype under different methylome modifying conditions (dCas9-
DNMT3a with sgRNAs targeting anti-correlated CpGs or the transcription start site (TSS),
and dCas9-TET1 with sgRNAs targeting correlated CpGs). We therefore selected RGMA for
3D screening of mitotic phenotypes upon epigenetic modification, using the same sample
preparation and imaging workflow as described above for the siRNA screen. Three and five
days after sgRNA plasmid transfection, every sgRNA transfected spheroid (as assessed by
GFP expression) was imaged and evaluated by Hoechst staining for mitotic phenotypes.
Most prominently, and in agreement with the MitoCheck database, macronuclei were detected
in 41-52% of all spheroids transfected with either dCas9-DNMT3a and sgRNA targeting anti-
correlated CpGs (Fig. 4A), dCas9-TET1 and sgRNA targeting correlated CpGs (Fig. 4B), or
dCas9 and dCas9-DNMT3a with sgRNA locating to the TSS (Fig. 4C,F). Transfection with
dCas9 control protein and sgRNA targeted to regulatory CpGs resulted in macronuclei in only
7% (anti-correlated CpGs) or 6% (anti-correlated CpGs) of spheroids (Fig. 4D,E), confirming
the specificity of our epigenetic modification of RGMA. Reduced spheroid growth, apoptotic
condensed DNA and elongated mitosis or mitotic arrest were also frequently observed,
indicating that RGMA knock-down has severe effects on cellular homeostasis (Fig. 4G).
Modulation of RGMA gene expression at the transcriptional level, using a dCas9-ED system,
and at the translational level, using siRNA, thus results in similar mitotic phenotypes which
can be detected with our high-throughput light-sheet imaging workflow, illustrating its
versatility.
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Discussion
The high-throughput light-sheet live imaging workflow presented here provides a novel tool
for screening individually treated 3D cell cultures with high spatial and temporal resolution,
signal-to-noise ratio, fast acquisition speed and minimal phototoxic effects. Automation of the
different steps from sample treatment and mounting to spheroid position detection and image
acquisition, as well as the commercial availability of all materials, ensure that the workflow is
reproducible and applicable to different culture models or treatment methods. Furthermore,
we provide an easy-to-use image processing pipeline adapted to the geometry of the dual-
view inverted light-sheet system, using a CNN for reliable cell cycle phase classification in
3D, which we have made available to the community online.
By applying this workflow to 3D cultured MCF10A H2B-GFP cells transfected with siRNA
targeting mitotic genes, and HEK293 cells transfected with dCas9-based epigenetic modifiers,
we were able to evaluate the effect of single gene knock-down on key cellular and spheroid
features. Our integrated high-content analyses also highlight similar phenotypes caused by
different genes. Due to the superior temporal and spatial resolution provided by the diSPIM
system in combination with long-term acquisition, we could track cells over 24 hours and
detect subtle mitotic 3D phenotypes not accessible with conventional fluorescent microscopy.
As the diSPIM geometry uses dipping lenses, phenotypes that can be evaluated by high-
throughput live imaging with this workflow are restricted to intracellular perturbations such as
siRNA or CRISPR-Cas9 based screens, although an end-point analysis of fixed samples can
also be conducted. To extend this workflow to other applications, such as using small
molecule libraries for high-throughput drug screens, samples need to be physically separated
into distinct wells during culture. Novel cell culture plate formats could accommodate this,
for example by mounting spheroids on invertible pillar structures for culture in multiwell
plates.
The light-sheet imaging setup presented here is thus adaptable for high-throughput screening
of 3D cell cultures in a variety of settings. As spheroids can be recovered after imaging, it is
also compatible with combined approaches correlating image data with other modalities,
including single-cell genomic or transcriptomic analyses. We therefore expect that the ability
to quantitatively evaluate 3D phenotypes in live cell cultures at high throughput will advance
functional characterizations of dynamic cellular processes in tissue-like microenvironments,
in cancer research and beyond.
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Materials and methods
Methods
Culture of MCF10A H2B-GFP cells
MCF10A cells (CRL-10317, ATCC, Manassas, Virginia, USA) transfected with a pBabe vector
containing a construct of GFP-labelled H2B were kindly provided by Zev Gartner and colleagues.
MCF10A H2B-GFP cells were between passage numbers 25 to 31 and routinely tested for
contamination. Cells were cultured in 2D in 25 cm2 culture flasks (Greiner bio-one, Kremsmünster,
Austria) in DMEM/F12 medium (ThermoFisher Scientific #11039, Waltham, Massachusetts, USA)
supplemented with 5% horse serum, 10 µg/ml Insulin (Life Technologies, Carlsbad, California, USA),
20 ng/ml EGF, 0.5 mg/ml hydrocortisone and 100 ng/ml Cholera Toxin (Sigma-Aldrich, St Louis,
Missouri, USA) under standard culture conditions (5% CO2 / 37 °C), and passaged after reaching 80-
90% confluency with 0.05% Trypsin (Life Technologies) every three to four days.
Solid-phase reverse siRNA transfection
Solid-phase reverse transfection siRNA transfection mix was prepared as described (Erfle et al.,
2008), but using trehalose dihydrate (Merck #T9531, Darmstadt, Germany) instead of sucrose. For
transfection, trypsinated MCF10A H2B-GFP were diluted in growth medium to a density of 5x105
cells/ml. 10’000 cells in 100 µl cell suspension were added to each well of the solid-phase reverse
transfection mix. After five hours, cell medium was removed and cells were resuspended by directly
adding 50 µl 0.25% Trypsin (Life Technologies #25200056) to each well.
High-content cell spotting in Matrigel
Mixing of cells with Matrigel (Corning, New York, USA) and spotting into OneWell plates (Greiner bio-
one CELLSTAR® OneWell Plate™ #670180) was conducted by an automated liquid handling robot
from Hamilton Robotics with a custom protocol. In short, from each cell suspension transfected with
individual siRNA, 60 isolated cells in 3 µl medium were mixed with 10 µl Matrigel. Subsequently each
mixture was spotted eight times with a single spot volume of 0.2 µl in a two columns by four rows
array, resulting in a total of 320 spots in 40 columns and eight rows on the imaging plate. One sub-
array of spots was always dedicated to beads (ThermoFisher Scientific #7220) mixed with Matrigel,
used for registration of the two acquired views. Positioning of each spot is identical with the positions
of a standard 1536-well plate. After 10 minutes at 37°C for Matrigel solidification, culture medium was
added and samples were incubated under standard culture conditions until imaging.
diSPIM imaging
Imaging was conducted with a dual-view inverted selective plane illumination microscope (diSPIM) as
described (Kumar et al., 2014). The microscope was equipped with LMM5 laser (Laser Illumination
Laser Merge Module 5, Spectral Applied Research, Richmond Hill, Canada) and Quad Filterset (F59-
405 / F73-410 / F57-406) purchased from AHF (Tübingen, Germany). Images were acquired by two
water-cooled ORCA-Flash4.0 Hamamatsu sCMOS cameras (Hamamatsu, Japan). Cooling was
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provided with Julabo F250 cooling circuit (Julabo, Seelbach, Germany). Standard culture conditions
were provided by an incubation chamber (3i ECS2) from Intelligent Imaging Innovations (Denver,
Colorado, USA), and direct airflow over the SPIM head was minimized to avoid unnecessary
vibrations. All imaging time lapse acquisitions were conducted with 320 µW laser power for 488 nm
excitation wavelength (measured at the sample). Readjusting the fine alignment of the microscope
was conducted shortly before the start of the acquisition.
Low resolution pre-screen
To detect each spheroid’s positions and select the spheroids to be imaged, we conducted a fast, low
resolution stage-scan pre-screen. A grid of imaging positions was defined across the imaging plate,
with each position placed at the center of one column of spots. As the automated spotting process
resulted in spots with defined positions and sizes, we were able to repeatedly use the same grid of
stage scan acquisition positions for every pre-screen. Potentially due to small manufacturing
differences of the imaging plate, we solely needed to adjust the general Z-position off-set, underlining
the robustness of our sample preparation process. Each position acquisition resulted in a Xmicroscope-
Stack of 1 200 slices with a step size of 5 µm, a pixel resolution of 0.648 µm/px and a field of view of
333 µm.
The acquisition of the pre-screen took 31 minutes and produced 96 000 images. This pre-screen data
was subsequently analysed by a KNIME image processing workflow detecting the XYZmicroscope
position, size and shape of each cell cluster. Per spot, we detected an average of 2.6 spheroids.
Small, flat and elongated cell clusters were excluded and the remaining spheroids ranked based on
their Z-position. To minimize obstructions in the illumination and detection path and maximize image
quality, the spheroids with the largest Z-coordinates were selected for imaging for each condition.
38 defined cell spheroids plus two positions with fluorescent beads (used for image processing) were
imaged with the diSPIM system for 24 hours at maximal temporal and spatial resolution for treatment
evaluation.
Position scan acquisition
Preselected positions from the KNIME analysis of the pre-screen were checked and if necessary
manually corrected. An additional registration position was added as first and last position, imaging
beads mixed in Matrigel.
Imaging parameters for dual view synchronous piezo/slice scan (stack acquisition) were set to acquire
two stacks of 1024 px2 in XYimage with the maximal camera resolution of 0.1625 µm/px centered to the
field of view of the camera and 260 slices in Zimage with a slice interval of 0.5 µm starting with view A
(right camera acquisition). Sample exposure was set to 1.5 ms and the option for “Minimized Slice
Period” enabled. The option for “Autofocus during acquisition” was enabled with the autofocus running
on the registration position imaging beads every acquisition cycle with 40 slices acquired every 0.5
µm. The off-set was detected by the “Vollath” algorithm.
Due to the acquisition limitations of the microscope of a minimal four to five seconds per position scan
and stage repositioning, we imaged at an interval of five minutes for 24 hours, resulting in a total data
volume of 10.06 terabytes (TB), which was stored locally. The high temporal resolution was essential
for tracking of nuclei as they progressed through the cell cycle. Throughout time lapse acquisition, we
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did not need to adjust for any position off-set introduced by deformation of the matrigel or external
influences as samples remained almost universally in the field of view.
Image processing (hSPIM)
Raw data was processed by a custom software named ‘hSPIM’ specifically adapted to the geometry of
the diSPIM and the separately acquired registration beads positions. In hSPIM, the registration matrix
of the two views is detected for each time point of the screen by registration of beads in 3D.
Additionally, the PSF is extracted. This registration matrix and PSF are stored and used for
registration and deconvolution of all other acquired positions at this time point. Furthermore, the
software performs a segmentation of the nuclei, from which different geometrical and textural features
are extracted for each segment. Deconvolved fusion images and segment images as well as segment
and feature table are stored and were used for further image analysis. In addition, the hSPIM software
can directly visualize in 3D a registered and deconvolved image snapshot, store a view angle, and
export a 3D movie of a single position. Library code and documentation for hSPIM are available at
https://github.com/eilslabs/diSPIM_screen.
High-content KNIME analysis workflow
Following the raw image processing, we developed a KNIME workflow to analyze key cellular and
global properties of each spheroid throughout the acquired time lapse. The workflow is available at
https://github.com/eilslabs/diSPIM_screen.
XYZmicroscope displacement: By tracking the positions of single beads over time, we could detect and
correct for the global offset in all dimensions of the microscope introduced through fine displacements
of the imaging plate or expansion of the diSPIM components.
Clustering of segments into spheroids: To segregate segments from two spheroids acquired at a
single imaging position into individual spheroid clusters, we analyzed the geometric distance of each
segment to all others and clustered segments accordingly.
Spheroid size: The clustering enabled us to combine all segments of one spheroid and determine
spheroid size.
Cell cycle phase classification: For precise cell cycle phase classification, we used a VGG-based
convolutional neuronal network trained on a set of manually classified images. The CNN calculated
the probability for each of the four cell cycle phases (interphase, prophase, metaphase, anaphase) for
each XY, XZ and YZ slice. The class with the highest sum in likelihood for each segment was selected
as the cell cycle phase of the nucleus at this time point.
Geometric nuclei class features: Single nuclei size, intensity, position of segments from the center
of the spheroid and predicted cell cycle class were recorded over time.
Nuclei migration speed: By tracking the position of each segment over time, we analyzed the median
migration speed of all cells in each spheroid.
Time lapse movie: For individual evaluation, we exported the maximum projected time lapse movie of
each position, including cell cycle classification and spheroid hull.
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Spheroid feature evaluation
Selected image features quantified by the KNIME workflow (Table S3) were subjected to further
quantitative analysis in R. Additional quantitative features such as mean cell volume (estimated as the
ratio of spheroid volume to nuclei number) and average nuclei size in different cell cycle phases were
computed. The fraction of cells detected in different cell cycle phases was averaged across all time
points. To calculate instantaneous spheroid growth rates from nuclei numbers, the number of nuclei
over time was smoothed using the lowess function with parameters f=1/3, iter=3L, delta=0.01 *
diff(range(NrCells[1:n.rows[[pos]],pos])), and differentiated using the diff function.
All feature measurements from all plates were combined into one matrix, centered by subtracting the
column means from their corresponding columns, and scaled by dividing the centered columns by
their standard deviations. As mechanical plate drift resulted in spheroids lying partially outside the field
of view in one plate, affected features (nuclei and spheroid growth rate, compactness, convexity,
sphericity, spheroid volume and cell volume) were excluded for this plate.
To identify clusters of siRNAs causing similar phenotypes, rank-based clustering was performed using
the rank, dist, and hclust functions. The number of clusters was chosen based on qualitative
assessment of morphologic similarity within groups. Heatmaps were created using the heatmap.2
function from the gplots package or the aheatmap function from the NMF package.
Statistical analysis
Since images were acquired of samples cultured in one-well plates with homogeneous culture
conditions, no randomization of siRNAs across the culture plate was necessary. Investigators were not
blinded during experiments or analysis. No statistical tests were used during data analysis.
dCas9-effector domains construct synthesis
We fused the catalytic C-terminal effector domains of epigenome modifying enzyme (DNMT3a, TET1)
C-terminally to the dCas9 via a linker and added two nuclear localization sequences (NLS) for
improved nuclear localization and a M2 flag to the N-terminus. A dCas9 with no C-terminal addition of
an ED was used as binding control and to physically block binding sites for regulatory factors. Source
constructs were obtained from AddGene (Watertown, Massachusetts, USA) for dCas9 (Gilbert et al.,
2013), DNMT3a (Vojta et al., 2016) and TET1 (Tahiliani et al., 2009). dCas-ED constructs (C49 –
dCas9; C54 – DNMTA3-dCas9; C57 – TET1-dCas9) were assembled by Gibson Cloning (NEB
#E2611) following manufacturer guidelines. Linker (GGGGS), NLS (PKKKRKV) and M2-Flag
(DYKDHDG) DNA sequences as well as adapter primers were ordered from Eurofins Genomics.
Successful cloning was assessed by sequencing by GATC Biotech AG, western blot (M2-flag) and
expression in HEK293 cells detected by immunostaining. Plasmid maps and construct components
are available from the authors on request.
Stable dCas9-ED expression in HEK293 cell line
5 x 105 HEK293 cells were transfected with 5 µg plasmid DNA of the different dCas9-ED constructs
(C49, C54, C57) with Lipofectamine 2000 (Invitrogen, Carlsbad, California, USA) following
manufacturer guidelines. 48 hours post dCas9-ED plasmid transfection, transfected cells were
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selected by addition of G418 (Geneticin, Sigma-Aldrich) antibiotic to the culture medium at a
concentration of 500 µg / ml. Stable dCas9-ED expressing cell lines were frozen after four passages
under G418 selection.
CpG selection
We targeted the different epigenome modifying molecular tools to specific genomic sites by combining
different sgRNAs with the different effector domains to modify distal or proximal regulatory CpGs with
regulatory properties (Table S1). Based on a data set comprising 450k Illumina gene expression and
CpG methylation data from human breast cancer patients (Parashar et al., 2018) available on the
UCSC genome browser (Kent et al., 2002), we selected CpGs with high correlation (Pearson
correlation > 0.5) or high anti-correlation correlation (Pearson correlation < -0.5) between CpG
methylation level and target gene expression. We detected up to seven correlated or anti-correlated
regulatory CpGs per target gene. Correlated CpGs (low CpG-me results in reduced expression) are
expected to result in a gene knock-down phenotype when targeted by the TET1 dCas9-ED, while anti-
correlated CpGs (high CpG-me results in reduced expression) are expected to show the abnormal
mitotic phenotype when targeted by DNMT3A. Target genes that had only a single regulatory CpG
with a correlation between expression level and CpG methylation below 0.6 were not further analyzed,
which excluded ATHOH8, AURKA, BUD31, CTSB, DSE, ESYT2, LGR4, RAN, and RBBP4.
Single guide RNA design and synthesis
sgRNAs directing the dCas9 effector domain fusion protein to the specific genomic site were designed
to direct the methylome-modifying enzymes to positions around 33 base pairs upstream from their
corresponding target CpG, since the dCas9-ED has been described to show highest epigenome
modifying effectivity at 27 bp (+/-17 bp) from the PAM sequence of the sgRNA (Vojta et al., 2016).
We designed two opposing sgRNAs per regulatory CpG, one binding to the sense and one binding to
the anti-sense strand of the DNA. Furthermore, sgRNA target sites had a minimum of two mismatches
to the next off-target site, to reduce off-targeting effects.
To evaluate gene knock-down through binding of the dCas9 without added effector domain to the
transcription start site (TSS), we used the FANTOM5/CAGE online atlas (http://fantom.gsc.riken.jp/5/)
to define the TSS of our target genes and selected a single sgRNA binding site at an average of 50 bp
upstream of the TSS for optimal gene repression (Gilbert et al., 2014; Radzisheuskaya et al.,
2016).
sgRNA expression plasmids were designed and synthesized following a previously published SAM
target sgRNA cloning protocol (Konermann et al., 2015). In short, the sgRNA(MS2) cloning
backbone (AddGene #61424) was digested with BbsI. Oligos representing the sgRNA target site with
20 bases in sense (Os) with a CACCG overhang and anti-sense (Oas) with an AAAC overhang were
ordered from Eurofins and annealed. For genome reference, we used the UCSC Genome Browser on
Human Feb. 2009 (GRCh37/hg19). Backbone and sgRNA defining insert were joined by a Golden
Gate reaction. The resulting plasmid was expanded by bacterial transformation and assessed by
sequencing.
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Stable dCas9-ED cell lines sgRNA transfection
The HEK293 cells were transfected with the different sgRNA constructs by solid-phase reverse
transfection as described (Neumann et al., 2010) but using Lipofectamine 2000 (Invitrogen
#11668027) instead of Lipofectamine RNAiMAX.
Immunostaining of HEK293 cells for DNA, sgRNA and dCas9-ED
HEK293 cells were fixed and stained at different time points between 3 and 9 days after transfection
for the two components of the functional dCas9-ED by immunofluorescence (IF) staining. Cells were
fixed with 4% PFA (Sigma-Aldrich #F8775) for 10 minutes in PBS with 0.5% Triton X-100 and blocked
subsequently with 1% goat serum in PBS applied overnight. Mouse anti-Flag M2 monoclonal primary
antibody (Sigma #F1804) and goat anti-mouse Alexa 568 secondary antibody (Invitrogen #A11004)
were used to label the dCas9-ED. Successful transfection with the sgRNA plasmid was detected with
rabbit anti-GFP monoclonal primary antibody (Cell Signaling Technology #2956, Danvers,
Massachusetts, USA) and goat anti-rabbit Alexa 488 (Molecular Probes #A11034, ). DNA was stained
with DAPI.
Confocal imaging of IF stained epigenome targeted HEK293 cells
Confocal imaging was conducted using the Zeiss LSM 780 (Zeiss, Oberkochen, Germany) with the
AutofocusScreen macro (http://www.ellenberg.embl.de/apps/AFS/, 24.02.2016), acquiring 25 Z-stacks
per well with each comprising five slices per dCas9-ED-sgRNA combination (one dCas9-ED / one
target gene). Each stack was acquired with a bright field image additionally to the DAPI (405 nm),
sgRNA (488 nm) and dCas9-ED (568 nm) channels.
2D pre-screen of dCas9-ED HEK293 cells and phenotype evaluation
In a 2D pre-screen designed to select for significant methylome regulated target genes, a total of 129
possible dCas9-ED-sgRNA combinations were evaluated with an average of 6856 cells analyzed per
combination. Solid-phase reverse transfection was used to deliver the sgRNA expressing plasmid into
the dCas9-ED expressing cell lines, where its expression was confirmed by GFP expression. We
evaluated and classified the nuclear phenotype of cells expressing dCas9-ED and the sgRNA at 3, 5
and 7 days post transfection.
Raw HEK293 images of each sgRNA-dCas9-ED combinations were smoothened by Gaussian
convolution and single nuclei were segmented by Otsu thresholding. Single segments were further
processed and split if necessary by segment erosion. Using the same CNN architecture as above, this
time trained on annotated images of 2D HEK293 cells, single nuclei were classified by into cell cycle
stages (inter-, pro-, meta-, anaphase) as well as significant phenotypes (macronuclei and apoptotic
condensed DNA). Additionally, the transfection state of the cell was evaluated by the presence of
dCas9-ED (M2-flag) and sgRNA (GFP), and only cells expressing both components were included in
the analysis.
Detected classes were further evaluated in comparison to non-targeted sgRNA transfected dCas9-ED
cell lines as well as to non-transfected cells. All acquired time points were combined during analysis.
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Cells with a significantly higher (> 1.5 fold) occurrence of a class compared to control cells were
highlighted.
We found that only 18% of sgRNA-dCas9 combinations showed a significant effect on the mitotic
phenotype, although 85% of those phenotypes correlated with the siRNA induced phenotype and 88%
correlated with knock-down phenotypes published in the online databases MitoCheck (Cai et al.,
2018) and Cyclebase (Santos et al., 2015) (Fig. S4). The targeted CpG properties matched with the
expected gene regulatory effect of the dCas9-ED in only 8/18 cases, suggesting that the majority of
abnormal mitotic phenotypes were evoked by the dCas9 protein blocking access to regulatory sites.
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Author contributions
BE and CC conceived the study; BE, TGK, HE, RE and CC designed experiments; BE, JB
and RB prepared, tested and conducted siRNA coating in 96-well plate; BE and JB developed
Hamilton liquid handling Matrigel/cell spotting protocol; CH defined targets for methylome
modification; BE conducted diSPIM imaging screen; BE and CC designed and wrote in
collaboration with mbits imaging GmbH the ‘hSPIM’ software; BE designed the KNIME
analysis workflow; BE and LA developed the deep learning classification model; BE and
TGK analyzed phenotypes; BE, TGK and CC wrote the manuscript. All authors revised and
approved the manuscript.
Acknowledgements
We thank Jon Daniels (Applied Scientific Instruments) for extensive technical support on the
diSPIM systems, Christian Dietz for support with the KNIME analysis pipeline, Katharina
Jechow (Theoretical Bioinformatics, DKFZ) for technical laboratory support, and Markus
Fangerau and Ingmar Gergel (mBits) for development and support of the ‘hSPIM’ library.
Results describing the high-throughput diSPIM screening workflow and mitotic knock-down
phenotype in this paper are reproduced from the PhD thesis of Björn Eismann (Ruprecht-
Karls-Universität Heidelberg, 2019).
Competing interests
No competing interests declared.
Funding
This study was supported by the German Network for Bioinformatics Infrastructure (de.NBI
#031A537A, #031A537C), the Helmholtz International Graduate School for Cancer Research
and the iMed Program (Helmholtz Association). TGK was supported by a DKTK
Postdoctoral Fellowship. The Advanced Biological Screening Facility was supported by the
CellNetworks-Cluster of Excellence, Heidelberg University (EXC81).
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Figures
Fig. 1: Key steps of the light-sheet high-content live imaging screen. (A) Step 1: Sample
preparation. Cells were transfected in 2D by solid-phase reverse transfection in a 96-well plate format
with two different siRNAs (siRNA set 1 and siRNA set 2) per target gene. Treated cells were then
mixed with Matrigel and spotted in 0.2 µl droplets onto a one-well imaging plate. Over five days of
culture, single cells clonally expanded into 3D spheroids. Each siRNA was analysed in triplicate in
three individual experiments. (B) Step 2: diSPIM imaging. In a low-resolution stage scan pre-screen,
positions of all spheroids were detected, and samples were selected for imaging by their position in
the Matrigel spots; fused spheroids or cells growing on the plate in 2D were excluded. Subsequently,
38 individually treated samples were imaged every 5 minutes for 24 hours by dual view light-sheet
microscopy, acquiring full stacks of view A (red) and view B (green) at a 90° angle to each other. (C)
Step 3: Data processing. Raw image data was processed by fusing visual information of view A and
view B. Processed data was further analyzed to evaluate the phenotype of each spheroid throughout
the time-lapse with regard to global spheroid features as well as single segment (single nucleus)
properties (scale bar = 30 µm).
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Fig. 2: Image analysis of diSPIM data to detect mitotic phenotypes in 3D spheroids. (A) For cell
cycle phase detection, 2D image slices of the 3D segments (scale bar = 5 µm) were used as input to a
VGG-based convolutional neuronal network consisting of convolutional, maxpooling and fully
connected layers as indicated (see also Fig. S2). The network outputs a probability for each of the cell
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cycle phases, with cross correlation values shown as a measure of classification accuracy (cross
correlation values represent 10% of the manually annotated training data set, with the other 90% used
for training the network). (B) Example time lapse (time points 175-187) of an untreated MCF10A cell
undergoing mitosis, with interphase (white), prophase (green), metaphase (yellow) and anaphase
(red) detected by deep learning image classification (scale bar = 5 µm). (C) Four exemplary time
points (t) of spheroid development imaged over 24 hours (time points 1-290), with the classified cells
(colors as in B), spheroid hull and segment maximum projection displayed (scale bar = 50 µm). (D)
Bar plot showing the total fraction of control nuclei detected in different cell cycle phases throughout
the screen (n = 205,068). (E) Violin plot depicting the distance of nuclei from the spheroid center
during the different cell cycle phases (n = 205,068). Black dots represent the median and whiskers the
25-75% interquantile range. (F) Examples of abnormal mitotic phase durations induced by different
siRNAs. Bar plots show the total fraction of nuclei detected in different cell cycle phases for negative
control samples transfected with non-coding siRNA (NC), as well as spheroids transfected with
siRNAs against INCENP, AURKA and PLK1. (G) Examples of spheroid growth defects depicted by
abnormal nuclei positions. Violin plots show the median distance of cells from spheroid centers during
the different cell cycle phases for the same spheroids as in F. Black dots represent the median and
whiskers the 25-75% interquantile range. Images show representative maximum and minimum sized
spheroids at the start of the time lapse acquisition (scale bar = 50 µm).
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Fig. 3: Clustered phenotype analysis of all features detected in diSPIM high-content screen. (A) Rank-
based hierarchical clustering of siRNA knock-down mitotic phenotypes by features describing the global and
nuclei specific properties results in distinct clusters of siRNA target genes, with the number of clusters chosen
based on qualitative assessment of morphologic similarity within groups. (B) Example images of spheroids from
each cluster with classified nuclear mitotic phases (white, interphase; green, prophase; yellow, metaphase; red,
anaphase). Scale bar = 50 µm.
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Fig. 4: Phenotype analysis of dCas9-ED targeting RGMA regulatory CpGs in 3D HEK293 spheroids.
Example images of HEK293 spheroids stably expressing dCas9-ED or dCas9, after transfection with sgRNA
designed to epigenetically alter RGMA expression, in the following combinations: (A) dCas9-DNMT3a with sgRNA
targeting anti-correlated CpG (cyan), (B) dCas9-TET1 with sgRNA targeting correlated CpG (magenta), (C)
dCas9-DNMT3a with sgRNA targeting the TSS (orange), (D) dCas9 with sgRNA targeting anti-correlated CpG
(cyan), (E) dCas9 with sgRNA targeting correlated CpG (magenta), (F) dCas9 with sgRNA targeting the TSS
(orange). Scale bar = 50 µm. (G) Summary of phenotypic effects of epigenetic targeting of RGMA expression in
HEK293 spheroids. The percentage of spheroids displaying abnormal cellular and global properties, including
macronuclei formation, extended mitosis duration, reduced spheroid growth and apoptotic condensed DNA
(ACD), is shown.
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J. Cell Sci.: doi:10.1242/jcs.245043: Supplementary information
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80 µm in diameter were imaged six days after seeding single cells in Matrigel. XY and XZ maximum
projections of the full 3D stack (scale bar = 50 µm) illustrate the XYZ resolution of the spinning disc and
diSPIM microscopes. Inserts show single nuclei close to the detection objective (green box) or imaged
80 µm inside the sample (red box) in XY (scale bar = 10 µm), with the position of the corresponding Z-
stack slices in the whole spheroid depicted by the red and green lines. (B) Example of an untreated
MCF10A H2B-GFP spheroid imaged over 156 hours / 936 time points (t) every 10 minutes from the two-
cell stadium to the fully developed spheroid in two channels (H2B-GFP and SiR-Tubulin dye). Scale
bar = 50 µm. (C) High temporal and spatial resolution enable the detection of distinct features of the
cytoskeleton and the different cell cycle stages (scale bar = 25 µm). (D) Mean fold enrichment of the
number of nuclei during a 24 hour acquisition cycle for imaged and non-imaged spheroids
(nimaged = 31 / nnon-imaged = 33). Error bars represent standard deviation.
J. Cell Sci.: doi:10.1242/jcs.245043: Supplementary information
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processing pipeline for random forest classification, and 2D (XY, XZ and YZ) slices of a 3D nucleus
image for deep learning classification (scale bar = 5 µm). Manual labelling of the same nuclei resulted
in a training data set comprising 21,359 data points for the RF classifier and 98,580 16-bit images for
the CNN. Cross correlations are shown as measurements of classification accuracy. In direct
comparison, classification differences between RF and CNN classification applied to the same image
can be visually identified (magenta arrows; scale bar = 50 µm).
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that resulted in a mitotic phenotype in HEK293 cells cultured in 2D. (B) Fraction of those phenotypes
that was also detected in the siRNA screen (top) or reported in the Cyclebase (CB) and/or MitoCheck
(MC) databases (bottom). (C) Example images of mitotic phenotypes evoked in 2D HEK293 cells upon
dCas9-ED localization to anti-correlated (cyan) or correlated (magenta) regulatory CpGs. (D) List of
target genes that showed a more than 1.5-fold increase in detection frequency of phenotypes (third
column), such as increased representation of individual cell cycle phases or apoptotic condensed DNA
(ACD), upon expression of dCas9 (top), dCas9-DNMT3a causing methylation (middle), or dCas9-TET1
causing demethylation (bottom) and transfection with sgRNA targeting anti-correlated (cyan) or
correlated (magenta) regulatory CpGs or the transcription start site (orange) of the gene (second
column). Detected phenotypes were compared with previously published databases (CB: Cyclebase,
MC: MitoCheck) and siRNA diSPIM screen results. Green triangles indicate spheroid and mitotic
phenotypes that were consistent across screens and sources.
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2. Supplementary Tables
Table S1: Selected target genes
Target genes were selected based on their association with the cell cycle, and correlation of their
expression with the methylation level of either correlated or anti-correlated CpGs with high average
absolute Pearson correlation value (Ravg).
Target gene Name Ambion
siRNA # Regulator
y CpG
Relation of CpG methylation to
gene expression Ravg
ATOH8 Protein atonal homolog 8
s39645 / s39643 1 anti-correlated 0.52
AURKA Aurora kinase A s196 / s197 2 correlated /
anti-correlated 0.52
BUD31 Protein BUD31 homolog
s17010 / s17009 1 correlated 0.45
CDC6 Cell division control protein 6
s2744 / s2746 2 anti-correlated 0.65
CDCA5 Sororin s41424 / s41425 6 correlated 0.70
CEP85 Centrosomal protein of 85 kDa
s34959 / s34961 5 correlated /
anti-correlated 0.64
CTSB Cathepsin B s3738 / s3739 1 anti-correlated 0.53
DSE Dermatan Sulfate Epimerase
s26749 / s26750 1 anti-correlated 0.54
EME1
Essential Meiotic Structure- Specific Endonuclease 1
s44946 / s44945 1 correlated 0.66
ESYT2 Extended synaptotagmin-2
s33138 / s33136 2 anti-correlated 0.75
F11R F11 Receptor s27152 / s27151 6 anti-correlated 0.55
FOXM1 Forkhead Box M1 s5250 / s5249 1 correlated 0.68
LGR4 Leucine-Rich Repeat G Protein- Coupled Receptor 4
s30840 / s229314 1 anti-correlated 0.61
LHFP Lipoma HMGIC Fusion Partner
s19847 / s19848 1 correlated 0.57
LMNB2 Lamin B2 s39477 / s39476 1 anti-correlated 0.60
LRP1 LDL Receptor Related Protein 1
s8278 / s8280 4 anti-correlated 0.70
MAP7 Ensconsin s17263 / s17262 2 correlated 0.67
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MEIS2 Meis Homeobox 2 s8666 / s8664 7 correlated /
anti-correlated 0.57
MYC Myc proto-oncogene protein
s9130 / s9131 2 anti-correlated 0.68
PLK1 Polo-like kinase 1 s448 / s450 4 correlated 0.64
PRC1 Protein regulator of cytokinesis 1
s17268 / s17269 1 anti-correlated 0.73
RAN GTP-binding nuclear protein Ran
s11769 / s11768 1 anti-correlated 0.60
RBBP4 Histone-binding protein RBBP4
s55169 / s56872 1 anti-correlated 0.57
RGMA Repulsive Guidance Molecule Family Member A
s32498 / s32500 7 correlated /
anti-correlated 0.70
TCF7 Transcription factor 7 s13877 / s13878 2 anti-correlated 0.71
TOP2A Topoisomerase II Alpha
s14307 / s14308 2 correlated 0.66
TUFT1 Tuftelin s14510 / s14509 2 anti-correlated 0.59
WBP1 WW Domain Binding Protein 1
s24095 / s225969 1 correlated 0.52
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Table S2: Comparison of spinning disc and diSPIM microscopy
Comparison of the acquisition properties and resulting image quality between a spinning disc
microscope (Zeiss LSM 780) and the diSPIM system. Light-sheet imaging outperforms spinning disc
microscopy in pixel resolution, acquisition speed, signal-to-noise ratio and phototoxicity.
Spinning disc microscope diSPIM
XYZ stack (px x px x slices) 1004 x 1002 x 233 2x (1024 x 1024 x 260)
pixel resolution 0.2 µm / px 0.1625 µm / px
laser power 1,320 µW 320 µW
exposure / slice 50 ms 1.75 ms
stack acquisition duration 53.2 s 4.5 s
(+ 15 s for image registration, merging and deconvolution)
signal-to-noise ratio 43.25 127
avg. background signal 18.4 0.941
power density / phototoxicity 168,000 W/cm2 40,700 W/cm2
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Table S3: 23 features describing spheroid phenotypes
Feature Name Description Global or nuclear feature
1 spheroid growth rate (nuclei) rate of increase in nuclei count over the course of the time lapse global
2 prophase ratio fraction of nuclei classified as “prophase” nuclear
3 metaphase ratio fraction of nuclei classified as “metaphase” nuclear
4 anaphase ratio fraction of nuclei classified as “anaphase” nuclear
5 avg. cell volume average cell volume (in voxels) as ratio of spheroid volume to nuclei number
global
6 prophase segment volume average nucleus size (in voxels) across all nuclei classified as “prophase”
nuclear
7 metaphase segment volume average nucleus size (in voxels) across all nuclei classified as “metaphase”
nuclear
8 anaphase segment volume average nucleus size (in voxels) across all nuclei classified as “anaphase”
nuclear
9 interphase segment volume average nucleus size (in voxels) across all nuclei classified as “interphase”
nuclear
10 spheroid volume spheroid volume (in voxels) throughout the time lapse global
11 avg. segment volume average nucleus size (in voxels) across all nuclei in all cell cycle phases
global
12 spheroid growth rate (volume) rate of volume increase of the spheroid hull throughout the time lapse
global
13 spheroid compactness factor describing the volume in relation to the largest extent global
14 convexity factor describing the volume in relation to the surface area global
15 nuclei migration speed average movement of all nuclei in 3D space in pixel per time point global
16 interphase transition duration average duration a nucleus spends in “interphase” nuclear
17 prophase transition duration average duration a nucleus spends in “prophase” nuclear
18 metaphase transition duration average duration a nucleus spends in “metaphase” nuclear
19 anaphase transition duration average duration a nucleus spends in “anaphase” nuclear
20 total number cell cycle transitions total number of deduced cell cycle phase transitions global
21 normal / abnormal transition fraction of cell cycle phase transitions that are biologically implausible global
22 spheroid roundness factor describing shape of spheroid global
23 size / spheroid roundness ratio ratio of spheroid volume to roundness global
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3. Materials Hardware
Workstation
hardware supplier description
CPU Intel, Santa Clara, California, USA i9-7980XE
GPU NVIDIA, Santa Clara, California, USA Titan xp 12 GB
Hard drive (RAID0) Western Digital, San José, California, USA WD-Red 8 TB
RAM AMD, Santa Clara, California, USA 64 GByte DDR-4 PC2400
Motherboard ASRock, Taipeh, Taiwan X299 Taichi
Controller Intel, Santa Clara, California, USA SATA Controller, 10x 6 Gbit/s
Hard drive Samsung, Seoul, South Korea 1 TB 960 Pro
ASI diSPIM hardware
hardware supplier description / number
camera cooling Julabo F250
quand filterset AHF F59-405 F73-410 F57-406
sCMOS cameras Hamamatsu ORCA-Flash4.0
laser Spectral Applied Research Laser Merge Module 5 (LMM5)
Software and workflows
Software
name version description
KNIME 3.5.5 Konstanz Information Miner
hSPIM 1.0 diSPIM raw image processing tool (‘hSPIM’)*
MicroManager 1.4 microscope control software
diSPIM plugin NB_20180116 nightly build MicroManager diSPIM controller plugin *hSPIM library available at https://github.com/eilslabs/diSPIM_screen
KNIME workflows (available at https://github.com/eilslabs/diSPIM_screen)
diSPIM_prescreen_stagescan_Pos_analysis
diSPIM_phenotype_screen_analysis_3D_spheroids
EpiTool_confocal_nuclei_classification
EpiTool_Class_quantitative_analysis
Haralick features used for phenotype characterization
Haralick feature description Haralick
feature description
F1 contrast F8 sum entropy
F2 angular second moment F9 entropy
F3 correlation F10 difference variance
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F4 sum of squares: variance F11 difference entropy
F5 inverse difference moment F12 measure of correlation I
F6 sum average F13 measure of correlation II
F7 sum variance
Source constructs
construct source description / number
#46911 AddGene Gilbert_pHR-SFFV-dCas (Gilbert et al., 2014)
#71666 AddGene pdCas9-DNMT3A-EGFP voj
#49792 AddGene FH-TET1-pEF (34) #61424 AddGene sgRNA(MS2) cloning backbone
Antibodies
description source number description
anti-Flag® M2 Sigma F1804 primary mouse anti Flag M2 monoclonal antibody
anti-GFP Cell Signaling Technology 2956 primary rabbit anti GFP monoclonal antibody
Anti-rabbit Alexa 488 Molecular Probes, Eugene, Oregon, USA A11034 fluorescent secondary goat anti
rabbit antibody
Anti-mouse Alexa 568 Invitrogen A11004 fluorescent secondary goat anti mouse antibody
Primary and secondary antibodies were used at a dilution of 1:2000. HEK cells not expressing dCas9-ED were used as a negative control for the anti-Flag M2 antibody signal. The anti-GFP antibody was solely used to enhance the GFP signal after fixation for imaging.
Consumables and solutions
description supplier product number
beads: PS-Speck™ Microscope ThermoFisher Scientific P7220 Cell Culture Plate, 96-Well Eppendorf, Hamburg, Germany 0030730119 CELLSTAR® OneWell Plate™ Greiner bio-one 670180 Cholera toxin Sigma-Aldrich (Merck) Collagen type IV solution Merck C5533 culture flasks (25cm2) Greiner bio-one DAPI Sigma-Aldrich (Merck) D9542 DMEM/F12 ThermoFisher Scientific 11039 G418 (Geneticin) Sigma-Aldrich (Merck) 4727878001 Gibson Assembly Master Mix NEB, Ipswich, Massachusetts, USA E2611 Insulin Life Technologies Lipofectamine 2000 Invitrogen 11668027 Lipofectamine® RNAiMAX ThermoFisher Scientific 13778075 Matrigel Corning 354248 OptiMEM ThermoFisher Scientific 51985026 PCR plate, 96 well Kisker Biotech, Steinfurt, Germany G060 trehalose dihydrate Merck T9531 trypsin Life Technologies 25200056
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