Post on 06-Aug-2021
iPS cell-derived cardiomyocytes:
overcoming barriers to therapeutic use
Chris GeorgeSwansea University Medical School
(i) A retrospective look at approaches for heart repair
: autologous bone marrow-derived SC
: cardiac-derived (ckit+) SC
(ii) Contemporary approaches
: ES-derived CM
: iPS-CM
(Direct fibroblast-to-CM conversion)
(v) Systematizing iPS-CM maturation
(iii) Problems with cell type and maturation
(vi) Cellular determinism: towards predicting and
controlling phenotype
(iv) Excitation-contraction coupling and the network
organization of cell signalling
(i) Repairing the failing heart with bone marrow-derived stem cells (BMSC): the end of the line?
• Just 5/49 trials using BMSC were free from „discrepancies‟ (~10%)
• In those 5, mean EF effect size was zero
Repairing the failing heart with cardiac-derived SC:
the fall-from-grace of c-kit+ lineage
2001 2017‘03 ‘04 ‘09 ‘10 ‘11 ‘12 ‘14 ‘15 ‘16
Lin-/c-kit+
haematopoietic cells
repair mouse heart
Nature (2001) 410:701-5
Cardiac-resident
c-kit+ cells repair
post-MI rat heart
Cell (2003) 114:763-76
BMSC do not become
cardiomyocytes
Nature (2004) 428:664-8 BMSC contribute to
revascularization of
the damaged heart
Nature (2004) 428:668-73BMSC do not become
cardiomyocytes
Nat. Med. (2004) 10:494-501No evidence of c-kit+
SC myogenesis
PNAS (2009) 106:1808-13
Neonatal, but not adult,
c-kit+ SC can become
cardiomyocytes
Circulation (2010) 121:1992-2000
Lancet (2011) 378:1847-57
Clinical benefit of c-kit+ cell transplantation
Adult c-kit+ SC adopt
vascular fate; neonatal c-kit+
SC can become
cardiomyocytes
PNAS (2012) 109:13380-5
Lancet Expression of concern 2014
Between 2 and 78 y/o human heart is regenerated
8-times from c-kit+-mediated myogenesis
Circulation (2012) 126:1869-81
Retraction of the „8-new-
hearts‟ story
Circulation (2014) 129:e466
Lancet (2014) 60608
Circulation (2013) 128:122-31
c-kit+ SC improve cardiac
function in HF
‘13
Nature (2014) 506:337-41
c-kit+ SC are irrelevant as a source of
new cardiomyocytes in vivo
Nat. Commun (2015) 6:8701
Lineage tracing confirms c-kit+ SC do
not become cardiomyocytes
Circ. Res. (2015) 116:1216-30
Paracrine effects of c-kit+ SCCirc. Res. (2016) 118: 17-19
Drawing a line under things?
Boston Globe
(ii) Beyond c-kit+ SC - ES-derived cardiomyocytes
Nature (2014) 510:273-7
1x109 cells (suspension, post-freeze
73±12% CM)
Direct myocardial injection
2 weeks 3 month Evidence of CM maturation in situ
2 weeks
x4
Diameter = 43mm
2 mm
Circ. Res. (2014) 115:335-8
Accentuate the negatives
1. Teratomer risk
2. Long term immunosuppression
3. 100% incidence of arrhythmias
(especially early on)
4. Arrhythmogenic risk increases at
slower heart rates (humans)
5. Is cardiac function improved?
Development of alternans
6. Long term toxicity of pro-survival
cocktail (sarcoma proteins)
1. Risk overplayed; 98% CM in graft;
zero teratomers in > 1000 rats
2. Universal donors / HLA engineering
3. Potentially due to poor coupling from
injecting 1x109 cells (< 1% cells remain);
Human heart may need ~ 8x109
4. Unlikely to see increased risk in humans
5. Further work needed
6. As point 1
The response
Circ. Res. (2014) 115:e28-9
Beyond ES-derived CM: grafting EHT built from iPS-CM
Sci. Trans. Med. (2016) 8:363ra148
EHT ‘seeded’ at 65% CM (atrial CM)
Day21 : 95% vCM @ 50bpm
2 weeks post-graft
4 weeks
post-graft
IPS-CM in EHT• ‘Immature’• In situ maturation
: sarcomeric extension by ~ 10% but still smaller than GP CM: >95% conversion of MLCA to MLCV isoform
• No arrhythmias reported• Human DNA in spleen (2/7) and lungs (4/7); none in liver or kidney
(iii) The futility of selecting nodal-, atrial- and ventricular-like cells…....?
Hallmarks of cardiomyocyte immaturity
Yang and Murry. Circ. Res. (2014) 114:511-523
Nature (2011) 471:68-73
IPS and epigenetic imprinting: they remember where they came from….....
iPS-CM ready for the clinic??
A network approach to cellular signalling
Out
In
Na+ K+
(iv) Excitation-contraction coupling and the entrainment of
surface membrane and intracellular SR „clocks‟
Na/K-
ATPase
NCXNa/K-
ATPasePMCACa2+
Plateau
“Trigger Ca2+”
RyR
SR
SERCA Myofilaments
Mito
Vinogradova et al. (2004) Circ. Res. 94:802-9
Inhibition of SR clock unmasks an
unusual Ca2+ entrainment in IPS-CM
Out
In
Na+ K+
Na/K-
ATPase
NCX Na/K-
ATPase PMCA Ca2+
“Trigger Ca2+”
RyR
SR
Sarcolemma
SERCA Myofilaments
Mito
X
X
Control
CPA
Edwards (unpub.)
Building a network….........
β-AR AC
Adr
ATP cAMP
Multiple downstream effectors
(“Fight or flight” response)
PKA
+
βγ
α
out
in
Activation via the the beta-adrenoceptor pathway
http://expasy.org/cgi-bin/show_thumbnails.pl
The cell signalling „blueprint‟ – everything linked to
everything else
///SR
Excitation-
contraction coupling
Ca2+
release
Mitochondria
Surface ion fluxes
Metabolism
Apoptosis
Protein synthesis & degradation
Intra-cellular synchronization
Inter-cellular synchronization
Gene expression
Horizontal and vertical network organization
out
in
cyto
lumen
Basal
Restore
Restore
Disrupt
Perturbing the homeostatic state : network adaptation
out
in
cyto
lumen
Unstable
Disrupt
Activated
out
in
cyto
lumen
Disrupt
Adapt out
in
cyto
lumen
Pseudo-stable
George et al. (2012) Am. J. Physiol. 303:897-910.
RyR2SERCA
LTCCNCX
Day
2 3 4 5 6 70
20
40
60
80
100
Tro
po
nin
po
sitiv
e
(%
)
2 3 4 5 6 70
20
40
60
80
100
Day
Ce
llula
r a
lig
nm
en
t (%
)
***
*
Day
Day 2 Day 4 Day 7
DAPI
Troponin-T
Alignment of ES-CM in culture
Lewis et al. J. Biomol. Scr. (2015) 20:30-40
Maturation-linked refinement of Ca2+ handling
5 sec
ΔC
a2
+
(20 f.u
.)
C
a2
+
(20
f.u
.)
Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Δ
2 sec
Temporal heterogeneity index (THI) = σ(int1….intn)
Amplitude heterogeneity index (AHI) = σ(ampa….ampn)
Inter-transient noise (ITN) = Σ(SV1….SVn)
SV1 SV2 SVn
int1 int2 intn
ampa ampb
ampn
Sa Sb Sb Sn
Time (t) Flu
ore
scen
ce
(units)
ampc
Frequency = S s-1
F0
Amplitude* = (ampn-F0) / F0
ka kb kc kn
da db dc dn
Duration = (da + db + dc + dn) / n
Area* = Area under curve / F0
Rate of decay = (ka + kb + kc + kn) / n
a b c n
a b c n
a b c n
a b c n
1
2
3
4
a b c n a b c n a b c n
a 1 1 0
b 0 1 0c 0 0 1
n 0 0 1
a 1 0b 0 1
c 0 0n 1 0
a 0
b 0c 0
n 0
S
Cell no.
1
2
3
2 3 4
= 8/24 (33.3%)
Co-incidence of S Coincidence of S = 33.3% (8/24)
Synchronization = Coincidence (Sa…..Sn)
Cell
S
SALVO-based profiling of cellular Ca2+ : discriminating
signals from noise
George and Edwards (2017) Stem Cell-Derived Methods in Toxicology pp173-90 Humana Press
Intracellular Intercellular
synchronization
D4 D5 D6 D70.0
0.2
0.4
0.6
0.8
Ra
te (H
z)
****
****
******
**
D4 D5 D6 D70.00
0.05
0.10
0.15
0.20
0.25
0.30
Am
plitu
de
he
tero
ge
ne
ity
in
de
x (A
HI)
****
****
****
D4 D5 D6 D70.0
0.1
0.2
0.3
0.4
0.5
Te
mp
ora
l h
ete
rog
en
eit
y in
de
x (T
HI)
****
****
********
D4 D5 D6 D70.000
0.005
0.010
0.015
0.020
Inte
r-tr
an
sie
nt n
ois
e (
ITN
)
*
****
**
D4 D5 D6 D70
10
20
30
40
50
Sy
nc
hro
niz
atio
n (%
)
Maturation of Ca2+ handling is linked to reduced Ca2+
signalling variability
Ra
te
Basal C
a2+
ITN
-A
ITN
-B
ITN
-C
Am
pli
tud
e
Du
rati
on
Are
a
Ra
te U
p
Tim
e-t
o-p
ea
k
Ra
te D
ec
ay
Tim
e-t
o-d
ec
ay
No
ise
in
dec
ay
TH
I
AH
I
0
50
100
SALVO parameter
% o
f m
ax
0.6 0.8 1.3 1.2 - 0.5 0.4 0.7 0.6 0.4 0.6 0.4 0.8 1.7 0.8CoV
n=209
Massively variable homeostatic Ca2+ handling
CoV = SD/mean
Jones et al (2016) Front. Cell Dev. Biol. 3:89
(v) Systematizing iPS-CM maturation : EB vs. monolayer
culture
EB maturation is not associated
with morphological change
Increased post-disaggregation clustering from older EBs
Defining conditions for maximum Ca2+ synchronization
Unmasking Ca2+ signalling phenotype
Switch [Ca2+]ext from
400μM to 1.8mM [Ca2+]ext
(vi) Cellular determinism
1
2
3
4
4
5
6
7
8
9
10
11
12
12
13
14
15
16
17
18
19
20
20
21
22
23
24
25
26
27
28
28
29
30
31
32
33
34
35
36
36
37
38
39
40
41
42
43
44
44
45
46
47
48
49
50
51
52
52
53
54
55
56
57
58
59
60
Cause Effect
Cell #
Towards predicting cellular response and heterogeneity
Homeostasis
State 1
State 2
State 3
Predictably influencing cell-to-
cell coupling in computational
arrays
Variability of
cell-to-cell coupling
Boileau et al. (2015) Ann. Biomed. Engin. 43:1614-25
59
103
Rotors in iPS-CM (Edwards, unpub)
Progress made
• Maturity of cells
• Maturity of field
• More realistic endpoints
Barriers that still exist
• Transparency / dataset availability /
understanding of „nuisance variables‟
• Reliance on crude end-points of phenotype and function
• High levels of phenotypic variability
• Unpredictability
• Unknown impact of reprogramming / source
What‟s needed?
• New „network‟ approach to cellular variability
and determinism
SUMMARY
Cardiff
Catrin Williams
David Lloyd
Adrian Porch
Dimitris Parthimos
Nottingham
Chris Denning
Gary Duncan
Divya Mirrington
David Edwards
Aled Jones
Sarah Marsh
Kimberley Lewis
Catherine Hather
Nicole Silvester
Steven Barberini-Jammaers
Phil Ashton
Archana Jayanthi
Alice Mitchell
Jessica Wells
Swansea (Engineering)
Perumal Nithiarasu
Etienne Boileau
Sanjay Pant
Ankush Aggarwal
SIRPA+/ VCAM+ SIRPA+/ VCAM-0
10
20
30
Pro
po
rtio
n o
f c
ells
(%
) *
102
103
104
105
102 103 104 105
VC
AM
-54
6 (
un
its
)
SIRPA-488 (units)
SIRPA+ / VCAM+
SIRPA+ / VCAM-
Non-e
nrich
ed
SIR
PA+ / V
CAM+
SIR
PA+ / V
CAM-
0
20
40
60
80
100
Tro
po
nin
po
sitiv
e (%
)
********
****
SIRPA+/VCAM-
SIRPA+/VCAM+
Non-enriched
TnT
DAPI
Does CM enrichment improve phenotype?
5 sec
ΔC
a2
+
(20
f.u
.)
Non-enriched SIRPA+/VCAM+ SIRPA+/VCAM-
Non-enriched SIRPA+/ VCAM+0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Ra
te
(no
rma
lize
d)
Non-enriched SIRPA+/ VCAM+0.0
0.2
0.4
0.6
0.8
1.0
1.2
AH
I
(no
rma
lize
d)
*
Non-enriched SIRPA+/ VCAM+0.0
0.2
0.4
0.6
0.8
1.0
1.2
TH
I
(no
rma
lize
d)
*
Non-enriched SIRPA+/ VCAM+0.0
0.2
0.4
0.6
0.8
1.0
1.2
ITN
(n
orm
alize
d)
****
Non-enriched SIRPA+/ VCAM+0.0
0.5
1.0
1.5
2.0
Sy
nc
hro
niz
ati
on
(no
rma
lize
d)
*
Non-FACS (Day 7)