Visualizing the complex spatiotemporal dynamics of human stem cells as they proliferate and make cell fate decisions is key to improve our understanding of how to robustly engineer differentiated tissues for therapeutic applications. In this webinar, Dr. Rafael Carazo Salas describes multicolor, multiday high-content microscopy pipelines that his group has recently developed to visualize the dynamical cell fate changes of human Pluripotent Stem Cells (hPSCs). In particular, he reviews the integrated experimental and computational approaches that his group has established, including novel “live” reporters of cell fate and multi-reporter hPSC lines generated by CRISPR/Cas9 allowing multiplexed monitoring of cell proliferation and fate dynamics, and exemplify the biological discoveries they are enabling. Key Topics Include: - Visualizing how human Pluripotent Stem Cells (hPSCs) proliferate and undergo early differentiation in vitro, by high content microscopy - Learning about experimental and computational pipelines that enable cell fate monitoring at the collective and single-cell level - Learning about novel “live” reporters of hPSC cell fate

1. Visualizing Human Stem Cell
Dynamics by Multicolor, Multiday
High-Content Microscopy
Rafael Carazo Salas, PhD
Professor
School of Cellular and Molecular Medicine
University of Bristol

2. Visualizing Human Stem Cell
Dynamics by Multicolor, Multiday
High-Content Microscopy
In this webinar, Dr. Rafael Carazo Salas describes
multicolor, multiday high-content microscopy
pipelines that his group has recently developed
to visualize the dynamical cell fate changes of
human Pluripotent Stem Cells (hPSCs).

3. Visualizing Human Stem Cell
Dynamics by Multicolor, Multiday
High-Content Microscopy
Copyright 2021 Rafael Carazo Salas, Yokogawa and InsideScientific. All Rights Reserved.
Rafael Carazo Salas, PhD
Professor
School of Cellular and Molecular Medicine
University of Bristol

4. Rafael E. Carazo Salas
University of Bristol, UK
carazosalaslab.org
Visualising human stem cell dynamics by multicolour,
multiday high-content microscopy
Webinar for Yokogawa Life Innovations, 02/2021

5. Yamanaka & Blau, Nature, 2010
Reprogramming Differentiation
The discovery of induced Pluripotent Stem (iPS) cells:
A new paradigm for Regenerative Medicine

6. iPS cell therapeutics: A reality within range?
Retinal tissue sheet from
the patient’s own iPS cells
Nature, 2014

7. Challenges with turning the paradigm into therapeutics
somatic
cells
iPS
cells
‘designer’
cells
reprogramming differentiation
Issues with efficiency, specificity & tumourigenic potential
Possible reasons:
•heterogeneity, both genotypic and phenotypic
•limited understanding of how to ‘program’ cells in a cause-effect manner
•limited information regarding what controls cell states & transitions beyond
transcriptional & epigenetic regulation (e.g. cell biological control?)
Del Sol, Imitola, Thiesen & Carazo Salas, Cell Stem Cell, 2017

8. iPS cells generated identically, and with equivalent
transcriptional status, differ in differentiation potential
www.hipsci.org Kilpinen et al, Nature, 2017

9. My group’s goal is to elucidate the quantitative & mechanistic
basis of efficiency, specificity & tumourigenic potential in
human Pluripotent Stem Cell (hPSC) differentiation to:
• Discover new molecular mechanisms controlling cell fate
• Establish quantitative and predictive standards for hPSC
culture and differentiation
• Use that knowledge to inform how to improve personalised,
safe human tissue engineering

10. Bulk analysis can be
misleading
Temporal trajectories directly
reveal causality
We aim to establish
temporal single-cell
lineaging to identify
‘cause-effect’ during
cellular programming

11. grow
divide
migrate die differentiate
maintain
X
How do all these events impact on how cells & tissues become
programmed, organized and stratified?
We aim to clarify
systematically the
role of cell biological
processes in
controlling cell fate

12. grow divide
migrate
die
differentiate
?
?
?
? ?
cell biological reporters:
cell cycle, polarity, signalling….
x, y, z, time info
cell fate/state reporters:
•pluripotency: Oct4, Nanog, ....
•differentiation: Sox2, Pax6, ....
?
pluripotency
Vision: Observe single-cell fate decisions for millions
of cells across generations & learn to predict fate

13. This talk
• Describe the technologies & capabilities we have
developed to visualise human stem cell dynamics
at scale
• Tell you about two ongoing projects based on
these technologies

14. Design of hPSC proliferation & cell fate reporters for CRISPR knock-in
Image processing, Machine Learning & statistical analysis of 1000s single-cells
Quantitative signatures of cell/line state & diagnostic predictors of fate
Large-scale hPSC culture & multiday, multicolour time-lapse microscopy
Comparison of properties across hPSC cell lines from different individuals
Identification of new mechanisms to optimise person-specific cell differentiation
Personalised cell/tissue QC & bioengineering
Our vision requires establishing experimental/computational
high-content microscopy pipelines to follow single-cell fate at scale
Design of hPSC proliferation & cell fate reporters for CRISPR knock-in
Image processing, Machine Learning & statistical analysis of 1000s single-cells
Quantitative signatures of cell/line state & diagnostic predictors of fate
Large-scale hPSC culture & multiday, multicolour time-lapse microscopy
Comparison of properties across hPSC cell lines from different individuals
Identification of new mechanisms to optimise person-specific cell differentiation
Personalised cell/tissue QC & bioengineering

15. For this we do ‘live’ multiday, multi-colour time-lapse
imaging using the Yokogawa CV7000
• Automated HT spinning disc confocal
for 2D/3D imaging in 4 channels+DPC
• Multi-well imaging with air (10x, 20x,
40x) & water (60x) objectives, for
tissue to subcellular resolution
• Timelapse imaging control of ToC,
humidity & CO2 for
hours/days/weeks

16. To visualize dynamical cell fate decisions we generate
cell lines expressing combinations of ‘live’ reporters
?
? migrate
die
x, y, z, time
info
grow divide
differentiate
?
? ?
cell biological reporters:
cell cycle, polarity,
signalling….
cell fate/state reporters:
•pluripotency: Oct4, Nanog, ....
•differentiation: Sox2, Pax6, ....
?
pluripotency
FUCCI
(cell cycle)
ERK, FOXO
(signalling)
Sakawe Sawano
et al, Cell 2008
Regot et al,Cell 2014
Maryu et al, Cell Struc
Funct 2016
H2B
(nucleus)
PCNA
(nucleus & cell cycle)
Grimm et al,
Nat Methods, 2015
Shcherbakova et al,
Nat Comms, 2016
Zerjatke et al,
Cell Reports, 2017
ORACLE
(cell fate)
novel reporter class
Carazo Salas lab,
Kim et al, bioRxiv, 2021

17. Example: Generating a FUCCI-expressing hPSC line
by targeted CRISPR knock-in
Collaboration with E. Piddini & L.
Vallier groups (Cambridge)
Sakawe Sawano et al, Cell 2008; Pauklin & Vallier, Cell 2013
red / green fluorescence

18. FUCCI allows monitoring cell cycle progression
‘live’ in hPSCs, at single-cell level
timelapse: 30’, duration: 3.5days

19. ‘Live’ imaging allows highly refined visualisation
of human stem cell dynamics
timelapse: 30’, duration: 2 days

20. ‘Live’ imaging allows highly refined visualisation
of human stem cell dynamics
(in agreement with Becker 2006, Watanabe 2007, Barbarić 2014, Phadnis 2015)
FUCCI + DRAQ7 + DPC

21. H2B-FarRed
OR
Generating hPSC cell lines co-expressing H2B-FarRed
reporter by CRISPR knock-in
far red fluorescence
Grimm et al, Nat Methods, 2015; Shcherbakova et al, Nat Comms, 2016

22. Co-expression of FUCCI & fluorescent H2B allows
simultaneously monitoring growth & division
timelapse: 10’, duration: 20h
FUCCI
H2B-HaloTag-JF646
digital phase contrast

23. timelapse: 10’, duration: 20h
FUCCI + H2B-HaloTag-JF646
Here is that
movie again,
now in three
colours ....

24. multi-colour imaging by differential time-lapse sampling
channel 5 min 10 min 15 min 20 min 25 min 30 min 35 min 40 min 45 min 50 min 55 min 60 min
far red
green
red
Optimised imaging protocols allow multiday imaging
while minimising phototoxicity

25. timelapse: 10’/30’, duration: 125h (neural trigger at time 0)
FUCCI + H2B-miRFP670
With these tools & capabilities we can uninterruptedly
monitor proliferation+fate dynamics for many days

26. 9 fields stitched, timelapse: 10’, duration: 6h
We detect & track cells and colonies over time by
customised image analysis pipelines

27. SVM classes
We automatically identify cell growth, cell division
and cell death events using machine learning

28. We automatically track and lineage cells and
their reporter signals using LEVER
Collaboration with A. Cohen (Drexel); https://bioimage.coe.drexel.edu/mp/lever/
Wait, Winter et al, Nat Protocols, 2011; Mankowski et al, BioImage Informatics, 2015

29. By tracking+lineaging we extract 100s
quantitative single-cell features, for 100000s cells
•Cell+nuclear size & geometry
•Cell movement
•Angle of cell division
•Mitosis or cell death status
•Colony that a cell belongs to
•Cell position within colony
•Local cell density
•Fluorescent reporter intensity/texture
•Colony size
•Colony movement
•Colony split/fusion
…. etc
Static/dynamic cell features
Static/dynamic colony features

30. Mapping multi-phenotypic heterogeneity at scale

31. pluripotent
early mesendoderm
early neuroectoderm
X1
X2
This allows us to obtain rich high-dimensional phenoprints of
different cell states & fate trajectories from 100000s of cells
Our goal is to use data-intensive methods & ML/AI to learn how to predict and control cell/tissue fate

32. Investigating cell division defects in hPSCs

33. hPSCs display tripolar and quadripolar mitotic
spindle defects

34. hPSCs display defects in chromosome segregation
H2B-miRFP670, timelapse: 10’, duration: 2h
lagging chromosome chromatin bridge

35. normal mitosis
bipolar spindle
chromosomal
bridge
lagging
chromosomes
multipolar
spindle
enlarged nucleus
micronuclei
(in agreement with Holubcová Z 2011, Acevedo-Acevedo S & Crone 2015, and Zhang J et al 2019)
hPSCs display a variety of severe cell division
defects similar to those of cancer cells

36. All defects can generate micronuclei, which are linked
with chromothripsis & tumourigenesis in cancer cells
lagging chromosome micronucleus tripolar spindle micronucleus

37. Mitotic defects can lead to cell death,
particularly for severe defects
MITOTIC PHENOTYPE
NORMAL
BIPOLAR
CHROMOSOMA
L BRIDGE
LAGGING
CHROMOSOME
TRIPOLAR
SPINDLE
QUADRIPOLAR
SPINDLE
Time in mitosis (min) 36.31 ± 6.37 39.77 ± 8.76 40.38 ± 10.66 66 ± 19.57 87.5 ± 9.57
Nucleus size (μm2) 156.8 ± 24.55 157.28 ± 27.59 161.35 ± 29.23 233.5 ± 63.48 278.12 ± 93.68
Daughter cell survival (%) 94.75 94.74 73.17 29.41 0
Daughter cell cycle time (h) 14.21 ± 1.99 13.64 ± 1.47 13.84 ± 1.47 14.72 ± 5.66 N/A
Number of events (n) 400 46 50 16 4
Event percentage of all
mitosis (%)
~99 0.14 0.12 0.04 0.01
n= 36,138 mitoses

38. G1 enrichment suggests mechanistic link to death triggered by unresolved
DNA damage
Cell death occurs through the subsequent cell
cycle, predominantly in G1

39. Most defects do not lead to cell death, providing
a route for genomic instability
MITOTIC PHENOTYPE
NORMAL
BIPOLAR
CHROMOSOMAL
BRIDGE
LAGGING
CHROMOSO
ME
TRIPOLAR
SPINDLE
QUADRIPOLAR
SPINDLE
Time in mitosis (min) 36.31 ± 6.37 39.77 ± 8.76 40.38 ± 10.66 66 ± 19.57 87.5 ± 9.57
Nucleus size (μm2) 156.8 ± 24.55 157.28 ± 27.59 161.35 ± 29.23 233.5 ± 63.48 278.12 ± 93.68
Daughter cell survival (%) 94.75 94.74 73.17 29.41 0
Daughter cell cycle time (h) 14.21 ± 1.99 13.64 ± 1.47 13.84 ± 1.47 14.72 ± 5.66 N/A
Number of events (n) 400 46 50 16 4
Event percentage of all
mitosis (%)
~99 0.14 0.12 0.04 0.01
We are currently investigating the mechanisms and consequences of this genomic
instability as it could underpin hPSC tumourigenic potential

40. Novel cell fate reporters for multiplexed ‘live’
visualization of cell fate dynamics

41. Filipczyk et al, Nat Cell Biol, 2015
Conventional cell fate reporters are nuclear and
incompatible with most fate/proliferation reporters

42. ORACLE: a new class of nuclear rim reporters
allowing visual ‘live’ monitoring of cell state
e
Kim et al, bioRxiv, 2021
knock-in at
Genomic Safe
Harbor locus
knock-in at
transcription
factor (TF)
locus
OR

43. ORACLE allows to quantitatively monitor
pluripotency dynamics ‘live’, at single-cell level
+ trigger (neural differentiation)
-trigger
ORACLE-OCT4 ORACLE-CAG timelapse: 30’, duration: 3 days

44. ORACLE allows to quantitatively monitor
pluripotency dynamics ‘live’, at single-cell level
pCAG
H2B
pOCT4
ORACLE
Kim et al, bioRxiv, 2021

45. Visualising the transition from pluripotency to
early neural fate commitment ‘live’
ORACLE-OCT4 ORACLE-SOX1 H2BmiRFP670, neural differentiation trigger
timelapse: 10’/2h, duration: 5 days Kim et al, bioRxiv, 2021

46. Visualising the transition from pluripotency to
early neural fate commitment ‘live’
pSOX1
ORACLE
pCAG
H2B
pOCT4
ORACLE
Kim et al, bioRxiv, 2021

47. Pluripotency exit and early neural differentiation dynamics
are heterogeneous and not tightly coupled, at single-cell level
Kim et al, bioRxiv, 2021
pSOX1
ORACLE
pCAG
H2B
pOCT4
ORACLE

48. ORACLE allows multiplexing with other nuclear
reporters of proliferation or fate
pCAG
H2B
pOCT4
ORACLE
Geminin
Cdt1
FUCCI
Kim et al, bioRxiv, 2021

49. Probing the link between cell cycle progression and
early pluripotency exit, at single-cell level
FUCCI ORACLE-OCT4 H2BmiRFP670, neural differentiation trigger
timelapse: 10’/30’, duration: 5 days Kim et al, bioRxiv, 2021

50. Pluripotency status and G1 length control are
quantitatively linked in hPSCs
(in agreement with Lee J et al 2010, Liu L et al 2019)
This suggests direct interaction between pluripotency and cell cycle machineries
that could clarify how early differentiation efficiency/specificity is controlled
Kim et al, bioRxiv, 2021

51. Summary
• We have established technologies to quantitatively study human stem cell
differentiation dynamics across generations, at single-cell level
• These allow us to elucidate mechanisms controlling stem cell differentiation,
and could be used to improve synthetic tissue design
• We have discovered cancer-like, non-lethal mitotic defects in hPSCs; these
could underpin tumourigenic potential in hPSC derived tissues
• We have established a novel class of cell fate reporters that allow ‘live’
visualisation of fate dynamics and transitions at single-cell level
• These reveal that the dynamics of pluripotency exit and early neural
differentiation are heterogeneous and not tightly coupled in cells

53. 1.To learn more and watch the webinar, visit:
www.insidescientific.com
2.Interested in learning more about Yokogawa’s solutions
for high content analysis? Visit: www.yokogawa.com
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