automated 3d light-sheet screening with high …...2020/04/10 · here, we developed a...

© 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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

JCS Advance Online Article. Posted on 15 April 2020

mailto:[email protected]

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

(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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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,

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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).

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

References

Angermueller, C., Pärnamaa, T., Parts, L. and Stegle, O. (2016). Deep learning for computational biology.

Mol. Syst. Biol. 12, 878.

Araujo, A. R., Gelens, L., Sheriff, R. S. M. and Santos, S. D. M. (2016). Positive Feedback Keeps Duration of

Mitosis Temporally Insulated from Upstream Cell-Cycle Events. Mol. Cell 64, 362–375.

Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Thiel, K. and Wiswedel,

B. (2009). KNIME - The Konstanz Information Miner. ACM SIGKDD Explor. Newsl. 11, 26–31.

Boutros, M., Heigwer, F. and Laufer, C. (2015). Microscopy-Based High-Content Screening. Cell 163, 1314–

1325.

Breiman, L. (2001). Random Forests. Mach. Learn. 45, 5–32.

Cai, Y., Hossain, M. J., Hériché, J.-K., Politi, A. Z., Walther, N., Koch, B., Wachsmuth, M., Nijmeijer, B.,

Kueblbeck, M., Martinic-Kavur, M., et al. (2018). Experimental and computational framework for a

dynamic protein atlas of human cell division. Nature 561, 411–415.

Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L.

A., et al. (2013). Multiplex Genome Engineering Using CRISPR/Cas Systems. Science (80-. ). 339, 819–

824.

Debnath, J., Muthuswamy, S. K. and Brugge, J. S. (2003). Morphogenesis and oncogenesis of MCF-10A

mammary epithelial acini grown in three-dimensional basement membrane cultures. Methods 30, 256–268.

Erfle, H., Neumann, B., Rogers, P., Bulkescher, J., Ellenberg, J. and Pepperkok, R. (2008). Work Flow for

Multiplexing siRNA Assays by Solid-Phase Reverse Transfection in Multiwell Plates. J. Biomol. Screen.

13, 575–580.

Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., Stern-Ginossar, N.,

Brandman, O., Whitehead, E. H., Doudna, J. A., et al. (2013). CRISPR-Mediated Modular RNA-

Guided Regulation of Transcription in Eukaryotes. Cell 154, 442.

Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C.,

Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014). Genome-Scale CRISPR-Mediated Control of

Gene Repression and Activation. Cell 159, 647–661.

Godinez, W. J., Hossain, I., Lazic, S. E., Davies, J. W. and Zhang, X. (2017). A multi-scale convolutional

neural network for phenotyping high-content cellular images. Bioinformatics 33, 2010–2019.

Haralick, R. M., Shanmugam, K. and Dinstein, I. (1973). Textural Features for Image Classification. IEEE

Trans. Syst. Man. Cybern. 3, 610–621.

Ho, T. K. (1995). Random Decision Forests. Proc. 3rd Int. Conf. Doc. Anal. Recognit. 1, 278–282.

Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. a and Charpentier, E. (2012). A programmable

dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–21.

Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M. and Haussler, a. D.

(2002). The Human Genome Browser at UCSC. Genome Res. 12, 996–1006.

Konermann, S., Brigham, M. D., Trevino, A. E., Joung, J., Abudayyeh, O. O., Barcena, C., Hsu, P. D.,

Habib, N., Gootenberg, J. S., Nishimasu, H., et al. (2015). Genome-scale transcriptional activation by an

engineered CRISPR-Cas9 complex. Nature 517, 583–588.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional

Neural Networks. Adv. Neural Inf. Process. Syst. 1097–1105.

Kumar, A., Wu, Y., Christensen, R., Chandris, P., Gandler, W., McCreedy, E., Bokinsky, A., Colón-

Ramos, D. A., Bao, Z., McAuliffe, M., et al. (2014). Dual-view plane illumination microscopy for rapid

and spatially isotropic imaging. Nat. Protoc. 9, 2555–2573.

Neumann, B., Walter, T., Hériché, J. K., Bulkescher, J., Erfle, H., Conrad, C., Rogers, P., Poser, I., Held,

M., Liebel, U., et al. (2010). Phenotypic profiling of the human genome by time-lapse microscopy reveals

cell division genes. Nature 464, 721–727.

Ntziachristos, V. (2010). Going deeper than microscopy: The optical imaging frontier in biology. Nat. Methods

7, 603–614.

Otto, T. and Sicinski, P. (2017). Cell cycle proteins as promising targets in cancer therapy. Nat. Rev. Cancer

17, 93–115.

Pampaloni, F., Reynaud, E. G. and Stelzer, E. H. K. (2007). Most of the cell-based data-harvesting efforts

that drive the integration of cell biology. Nat. Rev. Mol. Cell Biol. 8, 839–845.

Pampaloni, F., Chang, B. J. and Stelzer, E. H. K. (2015). Light sheet-based fluorescence microscopy (LSFM)

for the quantitative imaging of cells and tissues. Cell Tissue Res. 360, 129–141.

Parashar, S., Cheishvili, D., Mahmood, N., Arakelian, A., Tanvir, I., Khan, H. A., Kremer, R., Mihalcioiu,

C., Szyf, M. and Rabbani, S. A. (2018). DNA methylation signatures of breast cancer in peripheral T-

cells. BMC Cancer 18, 1–9.

Pelt, D. M. and Sethian, J. A. (2017). A mixed-scale dense convolutional neural network for image analysis.

Proc. Natl. Acad. Sci. U. S. A. 115, 254–259.

Pettet, G. J., Please, C. P., Tindall, M. J. and McElwain, D. L. S. (2001). The Migration of Cells in Multicell

Tumor Spheroids. Bull. Math. Biol. 63, 231–257.

Power, R. M. and Huisken, J. (2017). A guide to light-sheet fluorescence microscopy for multiscale imaging.

Nat. Methods 14, 360–373.

Preibisch, S., Amat, F., Stamataki, E., Sarov, M., Singer, R. H., Myers, E. and Tomancak, P. (2014).

Efficient bayesian-based multiview deconvolution. Nat. Methods 11, 645–648.

Pulecio, J., Verma, N., Mejía-Ramírez, E., Huangfu, D. and Raya, A. (2017). CRISPR/Cas9-Based

Engineering of the Epigenome. Cell Stem Cell 21, 431–447.

Radzisheuskaya, A., Shlyueva, D., Müller, I. and Helin, K. (2016). Optimizing sgRNA position markedly

improves the efficiency of CRISPR/dCas9-mediated transcriptional repression. Nucleic Acids Res. 44,.

Sadanandan, S. K., Ranefall, P., Le Guyader, S. and Wählby, C. (2017). Automated Training of Deep

Convolutional Neural Networks for Cell Segmentation. Sci. Rep. 7, 1–7.

Santos, A., Wernersson, R. and Jensen, L. J. (2015). Cyclebase 3.0: A multi-organism database on cell-cycle

regulation and phenotypes. Nucleic Acids Res. 43, D1140–D1144.

Schmied, C., Steinbach, P., Pietzsch, T., Preibisch, S. and Tomancak, P. (2016). An automated workflow for

parallel processing of large multiview SPIM recordings. Bioinformatics 32, 1112–1114.

Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition.

3rd Int. Conf. Learn. Represent.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

Soule, H. D., Maloney, T. M., Wolman, S. R., Brenz, R., Russo, J., Pauley, R. J., Jones, R. F., Brooks, S. C.

and McGrath, C. M. (1990). Isolation and Characterization of a Spontaneously Immortalized Human

Breast Epithelial Cell Line, MCF-10. Cancer Res. 50, 6075–6086.

Strnad, P., Gunther, S., Reichmann, J., Krzic, U., Balazs, B., De Medeiros, G., Norlin, N., Hiiragi, T.,

Hufnagel, L. and Ellenberg, J. (2016). Inverted light-sheet microscope for imaging mouse pre-

implantation development. Nat. Methods 13, 139–142.

Tahiliani, M., Koh, K. P., Shen, Y., Pastor, W. A., Bandukwala, H., Brudno, Y., Agarwal, S., Iyer, L. M.,

Liu, D. R., Aravind, L., et al. (2009). Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in

mammalian DNA by MLL partner TET1. Science (80-. ). 324, 930–935.

Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I.,

Tanouchi, Y., Ashley, E. A. and Covert, M. W. (2016). Deep Learning Automates the Quantitative

Analysis of Individual Cells in Live-Cell Imaging Experiments. PLoS Comput. Biol. 12, 1–24.

Vojta, A., Dobrinic, P., Tadic, V., Bockor, L., Korac, P., Julg, B., Klasic, M. and Zoldos, V. (2016).

Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 44, 5615–5628.

Wu, Y., Wawrzusin, P., Senseney, J., Fischer, R. S., Christensen, R., Santella, A., York, A. G., Winter, P.

W., Waterman, C. M., Bao, Z., et al. (2013). Spatially isotropic four-dimensional imaging with dual-

view plane illumination microscopy. Nat. Biotechnol. 31, 1032–8.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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).

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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).

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

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.

Jour

nal o

f Cel

l Sci

ence

• A

ccep

ted

man

uscr

ipt

J. Cell Sci.: doi:10.1242/jcs.245043: Supplementary information

Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

3

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.


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

5

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).


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

8

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.


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

9

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


CDCA5 Sororin s41424 / s41425 6 correlated 0.70

CEP85 Centrosomal protein of 85 kDa

s34959 / s34961 5 correlated /


CTSB Cathepsin B s3738 / s3739 1 anti-correlated 0.53

DSE Dermatan Sulfate Epimerase


EME1

Essential Meiotic Structure- Specific Endonuclease 1

s44946 / s44945 1 correlated 0.66

ESYT2 Extended synaptotagmin-2


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


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


MAP7 Ensconsin s17263 / s17262 2 correlated 0.67


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

10

MEIS2 Meis Homeobox 2 s8666 / s8664 7 correlated /


MYC Myc proto-oncogene protein


PLK1 Polo-like kinase 1 s448 / s450 4 correlated 0.64

PRC1 Protein regulator of cytokinesis 1


RAN GTP-binding nuclear protein Ran


RBBP4 Histone-binding protein RBBP4


RGMA Repulsive Guidance Molecule Family Member A

s32498 / s32500 7 correlated /


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


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

11

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


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

12

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


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

13

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


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

14

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


Jour

nal o

f Cel

l Sci

ence

• S

uppl

emen

tary

info

rmat

ion

automated 3d light-sheet screening with high …...2020/04/10 · here, we developed a...

Documents