the missing ingredient in cell functional diversity Bradly Alicea httpwwwmsuedualiceabr httpsyntheticdaisiesblogspotcom Typical four factors reprogramming eg iPS is inefficient and highly variable eg stochastic dynamics ID: 387656
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Slide1
Cellular decision-making bias:
the missing ingredient in cell functional diversity
Bradly
Alicea
http://www.msu.edu/~aliceabr/
http://syntheticdaisies.blogspot.comSlide2
Typical four factors reprogramming (e.g.
iPS) is inefficient and highly variable (e.g. stochastic dynamics). Rais et.al discover a way to make process deterministic.
Rais
et.al Deterministic direct reprogramming of somatic cells to
pluripotency
. Nature (2013)Slide3
Mbd3
+/- iPS lines (DOX-inducible cassette)
Host Blastocyst
(mouse)
Differentiate into MEFs
Reprogrammed to
iPS
(with latency)Slide4
In
Rais et.al (2013), “inefficiency” (the presence of un-reprogrammed cells) is characterized as a rate-limiting barrier.
Success!
(efficiency)
But what about these?
(1-efficiency)
How do you overcome rate-limiting factors?
1) Deplete Mbd3 (
nucleosome
remodeling and
deacetylation
repressor complex).
2) Promotion of naïve
pluripotency conditions.
Reprogramming factors exist in a dynamic equilibrium:
* Reactivate endogenous
pluripotency
networks (positive signal).
* Directly recruits Mbd3/
NuRD
repressor complex (negative feedback signal for reactivating this network).Slide5
Mbd3
+/- iPS lines (DOX-inducible cassette)
Host
Blastocyst
(mouse)
Differentiate into MEFs
Reprogrammed to
iPS
(with latency)
Reprogramming Latency
(per Hanna, 2009 and
Rais
, 2013)
Early
Reprogrammers
Late
Reprogrammers
t(
μ
)
Mbd3
f/-
is
necessary
but not
sufficient
(by itself) to achieve deterministic
reprogramming
time (
δ
)Slide6
ELITE
DEMOCRATIC
STOCHASTIC
DETERMINISTIC
B Cells, Hanna et.al, 2009
Fibroblasts,
Alicea
et.al, 2013
MUSE Cells,
Dezawa
et.al, 2013
MEFs
Rais
et.al, 2013
Differences in
cellular identity
Differences in
pathway regulationSlide7
Mbd3
is depleted, reprogramming efficiency promoted (using floxed and negative allele).
Mbd3
is expressed normally, efficiency is low and/or highly variable.Slide8
Even when
Mbd3 is depleted, factor expression (GFP+)
is still variable across colonies.Slide9
“Gas and Brakes” model: Figure 5, frame F
For more information, see: McDonel, P., Costello, I., and
Hendrich, B. Keeping things quiet: Roles of
NuRD
and Sin3 co-repressor complexes during mammalian development. International Journal of Biochemistry and Cell Biology, 41(1), 108-116 (2009).Slide10
From a systems perspective
Core
Pluripotency
Factors
Mbd3/
NuRD
repressor complex
( + )
( - )
“Gas and Brakes” model: Figure 5, frame F
For more information, see:
McDonel
, P., Costello, I., and
Hendrich
, B. Keeping things quiet: Roles of
NuRD
and Sin3 co-repressor complexes during mammalian development. International Journal of Biochemistry and Cell Biology, 41(1), 108-116 (2009).Slide11
Yet epigenetic regulation does not tell the whole story. Are there higher-level organizational factors at play?
Buganim
et.al, Cell, 150(6), 1209-1222 (2012).
Difference between
early
and
late
reprogramming:* early phase = core genes in pluripotency network exhibit mass upregulation
(genes act independently).* late phase = core genes in
pluripotency network exhibit hierarchical dependence (above).Slide12
Rais
et.al assumption: all cells reprogram to iPS, and occurs with uniform latency (no intrinsic differences in cell population).
Violation of assumption: what happens when cells exhibit variation? Or when one subpopulation is favored?Slide13
Question to keep in mind:
Is there a necessary relationship between the presence of a favored subpopulation
and reprogramming being a uniformly-distributed event?
iSMSlide14
The creation of “deterministic
reprogrammers” relies upon minimizing the variability in regulatory mechanisms (e.g. industrial process).* This is not normally found in nature, but systematic variation may exist between conversion regimens (e.g.
iN,
iSM
).
* I/O problem: transcription factor induction (input) and destination phenotype (output).
* are all forms of conversion equal, or are certain types of conversion (
iPS
, iN
, iSM,
iCM) easier to achieve?
Reprogramming bias:
tendency for some cell lines to favor a certain destination phenotype upon reprogramming.Slide15
Reprogramming Bias
Phenotypic (H1):* induced phenotype A vs. induced phenotype B (e.g. iNC
, iSMC
).
Genomic (H2 and H3):
* pre-existing bias, gene expression in different cell types before the transformative process.
* induced bias, gene expression after a transformative process has occurred.
Extrinsic (H4):
* tied to survivability of cells, does signal spectrum of a phenotype overlap with that of cells put under defined (survival) conditions?Slide16
Reprogramming Bias
H3(pre-existing bias)
H2
(induced bias)
H1
(phenotypic bias)Slide17
Building a signal spectrum (histogram):
* requires experimental replicates.* rank-order frequency method.
Sparse histogram:
* provides a multimodal distribution for further analysis.Slide18
Classical SDT
Signal and Noise are distinct
Signal and Noise overlap
Overlap =
d’
Signals are distinct
Signals overlap
Cellular SDT
Overlap =
O(
n,m
)Slide19
O(N,M) =
Σ MAX(Ni,Mi) - ||Ni – Mi||
OVERLAP
(N and M)
MAXIMUM (
i
th
element
N,
ith
element M)
Reprogramming Bias
Taken from a rank-order frequency spectrum for same cell lines.
FREQUENCY
RANK ORDER (CELL LINES IN ANALYSIS)
KIDNEY
HEART
OVERLAP
(N and M)Slide20
O(N,M) =
Σ MAX(Ni,Mi) - ||Ni
– Mi
||
Reprogramming Bias
Cell lines from some tissues (kidney, skeletal
muscle) show bias for one type of conversion
over another.Slide21
O(N,M) =
Σ MAX(Ni,Mi) - ||Ni
– Mi
||
Reprogramming Bias
Cell lines from some tissues (kidney, skeletal
muscle) show bias for one type of conversion
over another.
PROCESS DIAGRAMSlide22
Pre-existing Bias
Fibroblasts from 13 mouse fibroblasts cell lines known to exhibit differential reprogramming between muscle and neuron.
* high-throughput case (two breast and one lung line) exhibit no distinct pattern of bias, interesting (single probe) local differences.
Distributions are uniform with no tails, smear into one another (e.g. no bias).Slide23
Induced Bias
Human Fibroblasts under various drug treatments
Translatome
(Blue),
Transcriptome
(Red)
A = COL1A, B =
Fibronectin
, C = UTF
All three genes:
significant overlap for both fractions of RNA:* differences between genes: high-rank skew for COL1A, low-rank skew for UTF.
* COL1A, UTF: intermittent expression?
High-throughput case (fibroblasts under Vitamin C treatment):
* differences are inconclusive.Slide24
O(S,M) =
Σ MAX(Si,Mi) - ||Si – Mi||
OVERLAP
(S and M,
S and N)
MAXIMUM (
i
th
element
S, i
th element N or M)
Survivability
Taken from a rank-order frequency spectrum for same cell lines under
survival conditions.Slide25
O(S,M) =
Σ MAX(Si
,Mi
) - ||S
i
– M
i||
OVERLAP
(S and M,
S and N)
MAXIMUM (
i
th
elementS, i
th
element N or M)
Survivability
Taken from a rank-order frequency
spectrum for same cell lines under
survival conditions.
FREQUENCY
RANK ORDER (CELL LINES IN ANALYSIS)
KIDNEY
HEART
OVERLAP
(S and M)Slide26
2-dimensional Genotype Space
Naïve ground
state
iPS
iSM
iN
BIAS
BIAS
Schematic of a Random Walk, step size based on non-uniform distribution (semi-Levy Flight).
Stochasticity
w.r.t
. time
(
δ
)Slide27
12d
Reprogramming Model of
Rais et.al, 2013
(inducible factors)
4d
12d
Theoretical Maximum
Efficiency (e.g. 40%)
Kurtosis = efficiency of process (rate-limiting factors).
Skew = variability in
p
rocess.
time (δ
)Slide28
12d
Reprogramming Model of
Rais
et.al, 2013
(inducible factors)
4d
δ
12d
Model used here
assumes
that reprogramming events over time can be drawn from a Gaussian (e.g. uniform) probability distribution.
For each day, a certain proportion of cells convert. Above, 12d sees the maximum number of conversions.
Theoretical Maximum
Efficiency (e.g. 40%)
Kurtosis = efficiency of process (rate-limiting factors).
Skew =
stochasticity
in
p
rocess.Slide29
4d
12d
Is reprogramming according to a uniform distribution a reasonable assumption?
* model matches observations of reprogramming using inducible factors, but perhaps this has little relevance to the biology of process.
time (
δ
)Slide30
Conversion Rate
Infectability
Data (inducible YFP signal)
Mouse Cell Lines
4d
12d
4d
12d
Is reprogramming according to a uniform distribution a reasonable assumption?
* model matches observations of reprogramming using inducible factors, but perhaps this has little relevance to the biology of process.
time (
δ
)Slide31
Conversion Rate
Infectability
Data (inducible YFP signal)
Mouse Cell Lines
4d
12d
4d
12d
Is reprogramming according to a uniform distribution a reasonable assumption?
* model matches observations of reprogramming using inducible factors, but perhaps this has little relevance to the biology of process.
Converting to
iN
and
iSM
phenotypes results in variable distributions.
This suggests the reprogramming process might be better modeled using a exponential rather than a Gaussian.
time (
δ
)