Transcriptome dynamics along axolotl regenerative development are consistent with an extensive reduction in gene expression heterogeneity in dedifferentiated cells

An extensive reduction in gene expression heterogeneity is detectable early after axolotl forearm amputation

The axolotl has been a preferred model system for the study of post-traumatic regeneration of complex structures for more than a century (Voss, Woodcock & Zambrano, 2015). During this time, the progress of regenerative development responses has been intensively characterized macroscopically and microscopically (McCusker, Bryant & Gardiner, 2015a). Briefly, immediately after a gross insult such as the amputation of a limb, a number of processes are activated in response to the injury to heal the open wound (McCusker, Bryant & Gardiner, 2015a). Shortly after that, the amputation plane gets populated with regeneration-competent progenitor cells, collectively referred to as the blastema, which will coordinately grow and pattern to restore the missing structure (McCusker, Bryant & Gardiner, 2015a). A long-held view assumes that dedifferentiating cells are the main contributor to the formation of the blastema, and evidence of cell dedifferentiation upon injury have been found for different cell types such as muscle cells, keratinocytes or fibroblasts (McCusker, Bryant & Gardiner, 2015a). However, recent lineage-tracing analyses raised some doubts on how many cell types actually dedifferentiate in response to injury, and for those that do, how they contribute to the regeneration of the different tissues needed to restore the missing structure (Kragl et al., 2009; Sandoval-Guzman et al., 2014; Wu et al., 2015). Conversely, the study of heterogeneous samples from post-amputation axolotl limb blastemas have shown the activation shortly after amputation of genes commonly expressed in germ line cells, and transposable elements (TE) commonly silent because of their location in regions with highly compacted chromatin (Zhu et al., 2012a; Zhu et al., 2012b). Since TE activation is known to occur as a consequence of the extensive chromatin relaxation in un/dedifferentiated cells (Feher, 2015; Grafi & Barak, 2015; Macia, Blanco-Jimenez & Garcia-Perez, 2015; Wang & Wang, 2012), TE activations detected in axolotl regenerative development responses might be consistent with the possibility that the formation of the blastema required, at least partially, dedifferentiating cells that undergo extensive chromatin relaxation. Furthermore, Sosnik et al. (2017) recently developed a new method to quantify the nuclear condensation using microscopic images and demonstrated that blastemal cells show a significant reduction in chromatin condensation when compared with somatic dermal cells. Thus, the use of transcriptomic analyses for axolotl regenerative development models seems like an adequate place to seek preliminary support for the hypothesized extensive decrease in gene expression heterogeneity for processes that concur with cell dedifferentiation (Díaz-Castillo, 2017b).

In recent years, the number of studies of transcriptome dynamics for axolotl regenerative development models increased considerably (Bryant et al., 2017; Eo et al., 2012; Gearhart et al., 2015; King & Yin, 2016; Knapp et al., 2013; McCusker et al., 2015b; Monaghan et al., 2012; Monaghan et al., 2009; Pai et al., 2016; Ponomareva et al., 2015; Sabin et al., 2015; Seifert et al., 2012; Sousounis et al., 2014; Stewart et al., 2013; Voss et al., 2015; Wu et al., 2013). A common aspect to many of these studies is that they encompass very low levels of biological replication, i.e., 1–6 replicates per condition, which can constitute a big obstacle for the study of gene expression heterogeneity dynamics (see Materials and Methods, Fig. S1, and Table S1). For this reason, all analyses presented here used the dataset produced by the transcriptome study for axolotl regenerative development with the largest biological replication level. In 2015, Voss and coworkers analyzed transcriptome dynamics during the initial 28 days of axolotl post-amputation forearm regenerative development using 1 mm of heterogeneous tissue from the tip of amputated and post-amputational regenerating forearms at 19 timepoints (Voss et al., 2015). More importantly, the study performed by Voss and coworkers consisted of 9–10 biological replicates per timepoint for a region of post-amputational regenerating forearms where the blastema is formed and maintained along the regenerative process (Voss et al., 2015).

To characterize gene expression heterogeneity dynamics along axolotl forearm regenerative development, transcript abundance coefficient of variation (CV) was used as a measure of gene expression heterogeneity for each probeset and timepoint in Voss dataset. Also, a variation of the Monte Carlo-Wilcoxon matched-pairs signed-ranks test (MCW test hereinafter) was used to calculate the gene expression bias index (GEBI) that quantifies the bias for gene expression measures for a group of genes between two conditions and test their statistical relevance by comparing them with GEBIs calculated after randomly rearranging these measures (Fig. S2) (Díaz-Castillo, 2015). MCW test is particularly suited to study the existence of an extensive bias for a given quantitative measure between two conditions because it is sensitive to the number of elements within the studied dataset with biased measures and the extent of such biases, but also because it considers all the elements in the studied dataset instead of only those that are deemed significant in virtue of arbitrary thresholds (Díaz-Castillo, 2015). For this particular case, MCW test was used to confirm the existence of gene expression heterogeneity biases between each one of the post-amputational timepoints (TX) and the day of the amputation (T0), TX vs T0 pairwise comparisons. cvGEBIs, indexes calculated using transcript abundance CV, range from 1 if for all probesets under study transcript abundance CV was larger post-amputationally than in the day of the amputation (CV _TX > CV _T0), and −1 if for all probesets under study transcript abundance CV was lower post-amputationally than in the day of the amputation (CV _TX < CV _T0). To ascertain if observed gene expression heterogeneity biases for each TX vs T0 comparison were significantly different from those expected for pairwise comparisons with the same number of elements and value distribution just by chance, cvGEBIs were recalculated after randomly rearranging transcript abundance CV 10,000 times with no restriction. Since later on MCW tests with different designs will be used to address different proximate questions, the design with unrestricted permutation of gene expression measures will be referred to as unrestricted MCW tests.

Blastemas typically form 6–12 days after amputation depending on the animal size and age (Voss et al., 2015). If the blastema formation indeed encompassed intervening cell dedifferentiation, and this concurred with an extensive relaxation of chromatin, we would expect that cvGEBIs became negative and significantly different from those expected by chance from day 6–12 onwards. Somehow surprisingly, not only cvGEBIs were significantly different for the expected timepoints, but they started being significantly negative as early as 1.5 days after amputation (Fig. 1, and Table S2).

An intriguing aspect of gene expression heterogeneity dynamics along the regenerative development of axolotl forearm is that it is considerably variable itself (Fig. 1, and Table S2). Interestingly, cvGEBI variation is reminiscent of the progression of axolotl forearm regenerative development stages defined morphologically (Voss et al., 2015). In general, while significantly negative cvGEBIs tend to be higher for timepoints that correspond to the transition between regenerative development stages, and lower for intermediate timepoints for each stage (Fig. 1, and Table S2). For example, an increase in cvGEBIs can be observed in the transition between pre-bud and early bud stages, or medium and late bud stages, whereas the lowest cvGEBIs correspond to intermediate timepoints for medium bud and pallet stages (Fig. 1, and Table S2).

Voss and coworkers documented that samples corresponding to the same timepoint can be classified into consecutive morphological stages, and that this staging imprecision is more accentuated for timepoints corresponding to the transition between stages than for intermediate timepoints within each stage (Voss et al., 2015). An accentuated variation in the developmental progression of axolotl regenerative development is well known even under highly controlled conditions (Tank, Carlson & Connelly, 1976). Thus, the parallelism observed for cvGEBIs dynamics here and morphological changes in the original study for axolotl forearm regenerative development suggests that the methodology used here to analyze gene expression heterogeneity dynamics is sensitive enough to detect the effect of intrinsic factors contributing to gene expression heterogeneity such as axolotl regenerative development inherent asynchrony. Most importantly, despite the potential contribution of regenerative development asynchrony to gene expression heterogeneity, post-amputational cvGEBIs are significantly negative 1.5 days post-amputation onwards, as it would be expected if samples encompassing these timepoints were populated by dedifferentiated cells with extensively relaxed chromatin.

Whether the significant reduction in gene expression heterogeneity here reported truly reflected the presence of a significant number of dedifferentiated cells with extensively relaxed chromatin within post-amputational forearm blastemas would require independent corroboration using methodologies that go beyond the scope of this article. However, further analyses of the dataset under study resulted in other observations that favor this hypothesis over other obvious explanations for the observed decline in gene expression heterogeneity.

Gene expression heterogeneity dynamics along axolotl forearm regenerative development cannot be explained by a generalized increase in gene expression

In principle, the decrease in gene expression heterogeneity here detected for axolotl forelimb regenerative development could be explained without assuming that intervening cells undergo extensive chromatin relaxation. It has been shown that cells participating in regenerative processes tend to show a generalized increase in gene expression (Percharde, Bulut-Karslioglu & Ramalho-Santos, 2017). Since, gene expression heterogeneity is known to negatively correlate with gene expression level (Liu, Francois & Capp, 2016; Symmons & Raj, 2016), it would be expected that blastemal samples showed a reduction in gene expression heterogeneity regardless of intervening cells undergoing extensive chromatin relaxation.

To directly test whether the reduction in gene expression heterogeneity here detected resulted from blastemal cells generalized increase in gene expression, first, I proceed to study the dynamics of the level of gene expression for all genes in the dataset under study. Unrestricted MCW tests using transcript abundance mean per probeset and timepoint as proxy for their level of gene expression were used to quantify gene expression level biases across the transcriptome for TX vs T0 comparisons. GEBIs calculated using transcript abundance means are referred to as mGEBIs. Consistent with the possibility that blastemal cell showed a generalized increase in gene expression, mGEBIs become positive and significantly different from mGEBIs expected by chance 2 days after amputation (Fig. 1, and Table S2). In other words, genes tend to become significantly overexpressed post-amputationally starting 2 days after amputation. Furthermore, at a first glance, cvGEBI and mGEBI temporal dynamics are quasi-specular, underscoring the possibility that gene expression heterogeneity decrease along axolotl forearm regenerative development were just a consequence of blastemal cell generalized increase in gene expression. However, mGEBIs become significantly positive 2 days after amputation, with a small delay with regard to cvGEBIs becoming significantly negative 1.5 days after amputation. Such delay is more consistent with the possibility that blastemal cell generalized increase in gene expression was a consequence of intervening cells extensive chromatin relaxation that might contribute to the earlier significant decrease in gene expression heterogeneity, than the latter being a side consequence of blastemal cell generalized increase in gene expression.

To further study the interrelationship of the dynamics for gene expression level and heterogeneity along axolotl forearm regenerative development, three different variations of MCW tests for transcript abundance CV and TX vs T0 pairwise comparisons were used (Fig. S2). Unrestricted MCW tests were used to ascertain if observed biases for transcript abundance CV were significantly different from those biases expected just by chance. Timepoint-restricted MCW tests were used to ascertain if observed biases for transcript abundance CV were significantly different from those expected if gene expression heterogeneity was exclusively dependent on factors acting on the transcriptome as a whole. Finally, expression-restricted MCW tests were used to ascertain if observed biases for transcript abundance CV were significantly different from those expected if gene expression heterogeneity was exclusively dependent on the variation in the mean level of expression for each probeset. If post-amputational gene expression heterogeneity dynamics was just a mere reflection of blastemal cell generalized increase in gene expression it would be expected that observed cvGEBIs were not significantly distinguishable from simulated GEBIs for expression-restricted MCW tests. On the contrary, if post-amputational gene expression heterogeneity dynamics was dependent on factors acting on the whole transcriptome, it would be expected that observed cvGEBIs were not significantly distinguishable from simulated GEBIs for timepoint-restricted MCW tests.

Observed cvGEBIs were significantly different from simulated ones for all timepoints using any of the three MCW test designs (Fig. 2A, and Table S2), suggesting that chance, factors acting on the transcriptome as a whole, or the variation in the level of expression cannot explain on their own gene expression heterogeneity dynamics along axolotl forearm regenerative development. The proportional cumulative area under the curve (PC_AUC) using simulated cvGEBIs with the closest value to observed cvGEBIs for each timepoint and MCW test design was calculated to estimate how much of gene expression heterogeneity dynamics could be explained by chance, factors acting on the transcriptome as a whole, or the variation in the level of expression (Fig. 2B, and Table S4). The fraction of observed cvGEBIs dynamics that could be explained by the variation in the mean level of expression of each probeset under study is barely higher than what it could be explained just by chance alone (18% versus 16%). In stark contrast, factors acting on the transcriptome as a whole could explain up to 60% of observed cvGEBIs temporal dynamics. Considering the multidimensionality of the dataset, the potential interdependencies that might exist between its elements, i.e., genes that regulate other genes, or the effect of other factors that contributed to gene expression heterogeneity like developmental asynchrony, the fact that simulated cvGEBIs obtained using timepoint-restricted MCWs could explain 60% of observed gene expression heterogeneity dynamics is remarkable. These results strongly suggest that gene expression heterogeneity dynamics along axolotl forearm regenerative development cannot be easily attributed to blastemal cell generalized increase in gene expression. Instead, these results are more consistent with the possibility that axolotl forearm regenerative development concurred with an extensive decrease in gene expression heterogeneity associated to intervening cells undergoing extensive chromatin relaxation, which could be conducive for the expression increase of many genes throughout the genome.

Gene expression heterogeneity decrease for axolotl forearm regenerative development is consistent with blastemal cell dedifferentiation

The decrease in gene expression heterogeneity for axolotl forearm regenerative development here detected could be explained even if no intervening cell dedifferentiated. Whichever is the origin of blastemal cells, they are expected to encompass a more homogenous pool of cell types than those represented in samples taken at the moment of the amputation, and, immediately after that, when a diverse number of processes are elicited to heal the wound (McCusker, Bryant & Gardiner, 2015a). If the decline in gene expression heterogeneity here documented was caused by the relative enrichment of cells from a reduced number of cell types, it would be expected that genes supporting the earliest decline in gene expression heterogeneity resembled the transcriptional profile typical of the few cell types there represented.

To ascertain whether the extensive decrease in gene expression heterogeneity here detected was dependent on the relative enrichment in one or few cell types, I proceed to functionally characterized those probesets that better resemble the sharp decrease in gene expression heterogeneity between 1 and 1.5 days post-amputation (Fig. 1, and Table S2). Probesets in the dataset were ranked with regard to transcript abundance CV fold change for 1 and 1.5 days post-amputation timepoints. The genes corresponding to this prioritized list of probesets and 10 more lists of genes generated by randomly permutating probesets in the dataset with no other restriction were used to identify significant Gene Ontology (GO) enrichments using GOrilla and REVIGO (Eden et al., 2009; Supek et al., 2011). Only GO terms “mitotic cell cycle process” (GO:1903047) and “chromatin organization” (GO:0006325) appeared significantly enriched for probesets with a decrease in gene expression heterogeneity between 1 and 1.5 days post-amputation (Table 1, and S5). Also, just 10 genes were found supporting the enrichment of both terms out of the 155 genes that support either enrichment, suggesting that the sets of genes supporting each GO enrichment are largely different.

To further characterize the temporal dynamics of transcripts associated to “mitotic cell cycle process” and “chromatin organization” GO terms with regard to the whole transcriptome, I used a new variation of the MCW test. Functionally-restricted MCW tests proceed by calculating cvGEBI and mGEBI for probesets associated to genes classified in “mitotic cell cycle process” and “chromatin organization” GO terms before and after randomly rearranging functional subset tags for the whole dataset, respectively (Fig. S2). For both GO terms, the expected sharp decrease in transcript abundance CV between 1 and 1.5 days post-amputation is followed by a sharp increase in transcript abundance mean between 2 and 3 days post-amputation (Fig. 3, and Table S2). Consistent with these trends, the original characterization of the Voss dataset showed significant enrichments for GO terms related with cell cycle and chromosome organization for probesets with a statistically significant difference in transcript abundance between 2 and 3 days post-amputation timepoints (Voss et al., 2015).

“Mitotic cell cycle process” and “chromatin organization” GO term enrichment for probesets better supporting the sharp decrease in gene expression heterogeneity between 1 and 1.5 days post-amputation timepoints, and the transcriptomic dynamics of probesets for genes associated to these two terms along axolotl forearm regenerative development does not seem to be consistent with the possibility that early stages of regenerative development coursed with a relative enrichment in one or few cell types. Instead, these trends seem to relate better with the possibility that intervening cells undergo the earliest steps of transformations expected for dedifferentiating cells, namely re-entry in cell cycle and chromatin reorganization. If a reduction in gene expression heterogeneity followed by an increase in the expression of those genes that might drive the process of cell dedifferentiation reflected that chromosome regions encompassing these genes were the first to undergo chromatin relaxation for cells leading regenerative responses suggests that studies focused on the dynamics of the chromatin context for those genes that show the earliest decline in gene expression heterogeneity along regenerative development process could be of great help to better understanding how regenerative development is elicited in response to injury.

The study of gene expression heterogeneity can be useful for the characterization of processes that concur with cell dedifferentiation, and to engineer approaches for their monitoring and modulation

Whole transcriptome analysis is now a common tool to identify relevant changes in transcript abundance reflecting natural or experimental cues. Often, these analyses are based on the dubious assumption that significant changes in transcript abundance directly reflect equally significant changes in the function/effect of the very transcript or the protein it codes for. However, the study of transcript abundance dynamics permits directly addressing only a small fraction of the regulatory mechanisms needed for the generation of active genetic products. Transcript spatial distribution, and translation, or protein post-translational modification, folding, spatial distribution, and coordinated functionality with other genetic products remain largely unapproachable using only transcriptomic analyses. Since all these steps in the de novo synthesis of active genetic products are susceptible to be regulated independently from regulatory mechanisms modulating transcript abundance, it is possible that very obvious changes in transcript abundance are phenotypically insignificant because they are buffered by the system, or genes for which no significant change is detected at a transcriptional level had key roles in certain processes because of regulatory inputs acting at the protein level. This limitation is particularly troublesome when transcriptomic analyses are used to distinguish genes that drive a particular process from those that reflect the progression of the process in question, i.e., drivers versus effectors. While such distinction might be meaningless for the identification of diagnostic markers for biological processes, it is key for the basic characterization of such processes and the efficient engineering of strategies that afford in vivo monitoring or modulating their progress.

Gene expression heterogeneity is not directly informative about the abundance of active genetic products, and, therefore, its use to infer functional changes in the process under consideration is also very limited. However, gene expression heterogeneity dynamics might be highly informative about regulatory mechanisms acting on chromatin accessibility, transcription, and transcript degradation. In particular, the integration of gene expression heterogeneity dynamics with proxies for higher order chromatin organization could be of great value to identify chromosome regions encompassing genes that undergo the earliest changes along naturally-occurring or induced cell dedifferentiation, and the characterization of the regulatory elements mediating such changes. Furthermore, the possibility of identifying chromosome regions showing the earliest changes associated to cell dedifferentiation would afford using them as target for the insertion of engineered genetic constructs that help better monitoring or even modulating processes that concur with cell dedifferentiation. For example, in the context of regenerative development in response to injury, chromosome regions undergoing the earliest chromatin relaxation along cell dedifferentiation could be targeted for the insertion of genetic constructs coding for visible markers that helped identifying which cells within a complex tissue are susceptible to dedifferentiate in response to injury, or monitoring the progress of regenerative responses in non-invasive ways. Alternatively, the same regions could be targeted for the insertion of constructs coding for RNAs of proteins that by stimulating or interfering with key elements of cell dedifferentiation-based processes permitted modulating the course of such processes and improve their efficiency or minimize detrimental side effects.

A transitional reduction in gene expression heterogeneity for dedifferentiating cells could promote a long-term phenotypic heterogeneity

A troublesome aspect of processes that concur with cell dedifferentiation is the accentuated heterogeneity detected at genetic, epigenetic, and phenotypic levels (Almendro, Marusyk & Polyak, 2013; Burrell et al., 2013; Fossati, Jain & Sevilla, 2016; Krishna et al., 2016; Ling et al., 2015; Meacham & Morrison, 2013). Such heterogeneities are of great concern because they can limit the response to treatment of cancerous cell, or the reproducibility of healthy clonal tissues and individuals derived from animal and plant induced dedifferentiated cells (Almendro, Marusyk & Polyak, 2013; Burrell et al., 2013; Fossati, Jain & Sevilla, 2016; Krishna et al., 2016; Ling et al., 2015; Meacham & Morrison, 2013). Intriguingly, a chromatin relaxation-based extensive reduction in gene expression heterogeneity for dedifferentiated cells could promote long-term phenotypic heterogeneities following cell dedifferentiation by increasing genetic and epigenetic mutagenic potential and the phenotypic relevance of preexisting or newly generated mutations for dedifferentiated cells.

On one hand, an extensive chromatin relaxation-based surge of DNA accessibility might result in a larger number of coding or regulatory loci being susceptible to insults that result in genetic mutations, but also the activation of genetic elements that can directly intervene in mutagenic events such as those transposable elements populating regions of highly compacted chromatin. Indeed, abundant reports exist for accentuated genetic variation, and activities of transposable elements in processes concurring with cell dedifferentiation (Cooper et al., 2017; Jiang et al., 2011; Ling et al., 2015; Macia, Blanco-Jimenez & Garcia-Perez, 2015; Wang & Wang, 2012; Zhu et al., 2012a). Furthermore, the increased activity of repair machineries that help protecting the integrity of the genome in dedifferentiated cells might evidence their enhanced potential for genetic mutagenesis (Cooper et al., 2017). Also, 5-methylcytosine (5mC) is highly prone to spontaneously deaminate into thymine (T), which if uncorrected would cause both genetic and epigenetic mutations (Bellacosa & Drohat, 2015; Cortazar et al., 2007). The restoration of 5-methylcytosine is not straightforward and requires a concatenation of enzymes that recognize thymine:guanidine (T:G) mismatches, excise T, restore the original cytosine (C), and methylate the restored C (Bellacosa & Drohat, 2015; Cortazar et al., 2007). The key enzyme for 5mC deamination repair is thymine glycosylase (TDG), which is known to be considerably inefficient (Bellacosa & Drohat, 2015; Cortazar et al., 2007). The accentuated tendency to deaminate of 5mC, the complicated way of restoring 5mC, and the inefficiency of some of the elements required for 5mC restoration suggest that for any given methylated locus in a population of isogenic cells in the same environment there would be a variable assortment of cells showing 5mC, T, or C. Since the limiting step for 5mC deamination is DNA melting (Fryxell & Moon, 2005), processes more permissive for DNA melting, such as dedifferentiated cells extensive chromatin relaxation, could result in an accentuated and ultimately stochastic variation in DNA methylation and 5mC deamination-based mutations.

On the other hand, beyond the direct effect chromatin relaxation in dedifferentiated cells might have on the genetic and epigenetic mutagenic potential, a transitional chromatin relaxation-dependent extensive decrease in gene expression heterogeneity can also promote a long-term phenotypic heterogeneity by enhancing the phenotypic relevance of pre-existing and newly generated genetic and epigenetic mutations. Genetic capacitance refers to the accumulation and release of genetic variation in a cryptic state, i.e., not causing phenotypic variation (Gibson & Reed, 2008; Masel & Trotter, 2010; Paaby & Rockman, 2014). In recent years, it has been suggested that chromatin compaction-dependent gene expression heterogeneity promotes genetic capacitance because it results in a spectrum of stochastic phenotypes from which some genetic-based phenotypes might be indistinguishable (Díaz-Castillo, 2015; Díaz-Castillo, 2017a). Such cryptic genetic variation would be undetectable for selective forces, and, therefore allowed to fluctuate randomly within biological populations. Generalized differences in chromatin compaction modulating gene expression heterogeneity, and, with it, genetic capacitance, have been proposed to importantly contribute to sexually dimorphic traits in metazoans and differences in the spatiotemporal dynamics of natural populations from species with different amounts of junk DNA (Díaz-Castillo, 2015; Díaz-Castillo, 2017a).

If cell dedifferentiation truly concurred with an extensive decrease in chromatin compaction-dependent gene expression heterogeneity, it could be expected that such decrease resulted in a reduction in genetic capacitance (Fig. 4). In other words, due to the extensive decrease in gene expression heterogeneity, the spectrum of stochastic phenotypes would become narrower, and, consequently, preexisting cryptic or new genetic and epigenetic mutations would become phenotypically relevant (Fig. 3). Revealed genetic and epigenetic mutations would contribute to a phenotypic plasticity in dedifferentiated cells noticeable in the absence of any other cue, or upon signaling from their environments or the action of external treatments. The growing literature on the relevance of stochastic events commonly occurring in early development could support the possibility that un/dedifferentiated cells showed limited genetic capacitance because, at least in part, their extensive chromatin relaxation, and, therefore, being more susceptible to manifest plasticity inherently and/or in response to external cues (Losick & Desplan, 2008; Vogt, 2015).

In summary, an extensive reduction in gene expression heterogeneity for dedifferentiated cells is not only not conflicting with the multiple reports on accentuated genetic, epigenetic, and phenotypic heterogeneities for natural or induced processes that concur with cell dedifferentiation, but it could be argued that the extensive chromatin relaxation typical of dedifferentiated cells might be the ultimate cause for these apparently opposing heterogeneity trends.