Cellular decision-making bias: the missing ingredient in cell functional diversity Bradly Alicea

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)

Mbd3 +/- iPS lines (DOX- inducible cassette) Host Blastocyst (mouse) Differentiate into MEFs Reprogrammed to iPS (with latency)

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).

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 to achieve deterministic reprogramming time (δ)

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 regulation

Mbd3 is depleted, reprogramming efficiency promoted (using floxed and negative allele). Mbd3 is expressed normally, efficiency is low and/or highly variable.

Even when Mbd3 is depleted, factor expression (GFP+) is still variable across colonies.

“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), (2009).

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), (2009).

Yet epigenetic regulation does not tell the whole story. Are there higher-level organizational factors at play? Buganim et.al, Cell, 150(6), (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).

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?

Question to keep in mind: Is there a necessary relationship between the presence of a favored subpopulation and reprogramming being a uniformly- distributed event? iSM

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.

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?

Reprogramming Bias H3 (pre-existing bias) H2 (induced bias) H1 (phenotypic bias)

Building a signal spectrum (histogram): * requires experimental replicates. * rank-order frequency method. Sparse histogram: * provides a multimodal distribution for further analysis.

Classical SDT Signal and Noise are distinct Signal and Noise overlap Overlap = d’ Signals are distinct Signals overlap Cellular SDT Overlap = O(n,m)

O(N,M) = Σ MAX(N i,M i ) - ||N i – M i || OVERLAP (N and M) MAXIMUM (i th element N, i th element M) Reprogramming Bias Taken from a rank-order frequency spectrum for same cell lines. FREQUENCY RANK ORDER (CELL LINES IN ANALYSIS) KIDNEYHEART OVERLAP (N and M)

O(N,M) = Σ MAX(N i,M i ) - ||N i – M i || Reprogramming Bias Cell lines from some tissues (kidney, skeletal muscle) show bias for one type of conversion over another.

O(N,M) = Σ MAX(N i,M i ) - ||N i – M i || Reprogramming Bias Cell lines from some tissues (kidney, skeletal muscle) show bias for one type of conversion over another. PROCESS DIAGRAM

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).

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.

O(S,M) = Σ MAX(S i,M i ) - ||S i – M i || 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.

O(S,M) = Σ MAX(S i,M i ) - ||S i – M i || 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. FREQUENCY RANK ORDER (CELL LINES IN ANALYSIS) KIDNEYHEART OVERLAP (S and M)

2-dimensional Genotype Space Naïve ground state iPS iSM iN BIAS Schematic of a Random Walk, step size based on non- uniform distribution (semi-Levy Flight). Stochasticity w.r.t. time (δ)

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 process. time (δ)

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 process.

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)

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)

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 should be modeled using a exponential rather than a Gaussian. time (δ)