Stem Cell Chronicles: Autobiographies Within Genomes
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- Shibata, D. & Tavaré, S. Stem Cell Rev (2007) 3: 94. doi:10.1007/s12015-007-0022-6
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Human stem cell studies are difficult because many of the powerful experimental approaches that mark and follow stem cells and their progeny are impractical. Moreover, humans are long-lived, and it would literally take a lifetime to follow stem cell fates prospectively. Considering these hurdles, an ideal method would not require prior experimental manipulations but still allow “observations” of human stem cells from birth to death. The purpose of this review is to outline how histories or fates are likely to be surreptitiously recorded within somatic cell genomes by replication errors (molecular clock hypothesis). It may be possible to reconstruct stem cell lifetimes by measuring the random somatic changes that accumulate within their genomes, or the genomes of their more-easy-to-identify progeny.
KeywordsStem cellMolecular clockGenealogy
Life involves the replication and transfer of information between generations. Information is stored within genomes, which also record ancestry because genomes are almost exact copies of copies. Replication errors inevitably occur, allowing for variation or evolution, and these changes may subsequently be copied and passed from cell to cell. Ancestry is recorded by such random changes, and it is possible to infer intervals since genomes shared a common ancestor by counting differences—the greater the differences between two genomes, the greater, on average, the interval since they shared a common ancestor (“molecular clock” hypothesis ).
Stem Cell Genealogies
Before dwelling on the complexities of “molecular clocks,” it is useful to outline how stem cell biology is logically encoded by mitotic age and genealogy (the changes in phenotype between the zygote and a present day cell). The genealogy and genome of every cell starts from the zygote. The genealogy of many cells can be divided into three sequential phenotypic phases: development from the zygote, a stem cell phase, and differentiation (Fig. 1b).
Development and differentiation are programmed and restricted to specific times and numbers of divisions. For many cell types, development only occurs during the first few months or years after conception, and differentiation from a stem cell also typically requires a set amount of time from several days to weeks. Numbers of divisions during these phases are pre-programmed or “constant” regardless of adult chronological age. For example, colon morphogenesis is essentially completed by birth and differentiated colonocytes are produced daily and survive about a week . By contrast, stem cell mitotic ages may vary because of their potential for limitless divisions. Therefore, changes in the mitotic ages of differentiated cells logically reflect changes in stem cell mitotic ages (Fig. 1c). The mitotic ages of easy-to-collect differentiated cells can potentially reveal how often hard-to-identify stem cells divide.
Epigenetic Molecular Clocks
Unlike germline sequences that may differ between individuals, the “starting” CpG methylation state for everyone is relatively uniform because methylation is actively and passively removed or “erased” early in development before implantation . Essentially everyone starts life without CpG methylation, and therefore it is possible to observe “serial” methylation changes by sampling individuals of different chronological ages. Re-methylation can be broadly divided into programmed changes during development or differentiation, and random replication errors that may occur during any genealogical phase. About half of all human genes have CpG rich regions near or at their promoters, and extensive methylation of these CpG islands is associated with gene silencing [6, 10]. Methylation functionally involved in the control of expression is likely to occur to the same extent in a differentiated cell type, and therefore not vary with chronological aging or numbers of stem cell divisions.
Epigenetic Molecular Clock Data
With species molecular clocks, the same sequence can be used to compare widely different species . In theory, somatic cell molecular clocks may similarly accumulate replication errors regardless of cell type, allowing comparisons between different human tissues. Methylation with chronological age was measured for colon, small intestines, endometrium, brain, hair follicles, and neutrophils (Fig. 6). Consistent with an unmethylated start, CSX tag methylation is low early in life for all tissues. The brain tests whether tag methylation is a function of mitotic or chronological age because cell division is rare after childhood. Consistent with in vitro studies that suggest division is required for de novo methylation , the brain exhibits a static genealogy with low methylation in fetal tissues, and higher but stable adult tag methylation (unpublished data). Colon  and small intestinal epithelium  exhibit continuous genealogies with tag methylation increasing with chronological age, suggesting crypt stem cells divide throughout life. Endometrial epithelium also exhibits a continuous genealogy with increases in methylation before menopause, but methylation levels do not significantly increase after menopause when cell division largely ceases . These studies are consistent with the hypothesis that average numbers of methylated sites are proportional to numbers of replication errors or mitotic ages.
Hair epithelium is mitotic but hair follicle tag methylation did not significantly increase with aging . Hair biology  is consistent with a punctuated genealogy because the primary mitotic compartment (bulb) is physically separated from the stem cell compartment (bulge). Human hair exhibits cycles of follicle bulb growth and degeneration every few years. Bulge stem cells divide infrequently and primarily at the start of a new hair cycle to produce differentiated cells that migrate out of the bulge to reform a new hair follicle bulb. Differentiated follicle cells divide and accumulate replication errors, but these errors are discarded at the end of a hair cycle when the follicle bulb disappears. In this way, hair follicles may have similar mitotic ages regardless of the age of individual because new hair follicles originate from bulge stem cells that divide only at the start each cycle. The lack of a measurable age related increase in replication errors may reflect relative bulge stem cell mitotic quiescence, or that the bulge contains a pool of stem cells that successive repopulate the bulb (clonal succession ).
Neutrophils are released daily from the bone marrow and survive less than a day in the blood. A punctuated genealogy might also be observed with hematopoiesis because its niche is adjacent to bone , which is constantly remodeled. There appears to be a stem cell pool because hematopoietic stem cells normally circulate in the blood . Average neutrophil mitotic ages may not normally increase because newly formed niches may be successively colonized by relatively young stem cells, even in older individuals. Consistent with a punctuated genealogy, no significant increase in neutrophil tag methylation was observed during chronological aging (Fig. 6, unpublished data). Episodic destruction and reformation of a mitotic compartment, as with hair and during hematopoiesis, may be a physical prerequisite for a punctuated genealogy because glands that persist exhibit continuous genealogies.
Reconstructing the ancestry encrypted within genomes requires multiple comparisons because random replication errors will produce seemingly random 5′ to 3′ differences (Fig. 5b). The significance of such tag patterns becomes clearer by examining multiple tissues of different ages, realizing that all genomes are copies of copies. The empirical data illustrated in Fig. 6 are consistent with the idea that numbers of genome replications and genealogy are the primary mechanisms responsible for the tag patterns found in many somatic cells. Random genome patterns can be the “words” in a replication language.
“Real” Stem Cells are “Ghosts”
Stem cells defined by ancestry are “ghosts” because they are no longer are physically present, but their progeny make them “real”. By contrast, prospective definitions of stem cells depend on the future. Experiments characterize what may happen when cells are manipulated whereas decades of normal in vivo divisions can be recorded within genomes. For example, genomes in a 101 year-old individual potentially chronicle the lives of long dead stem cells from over a century ago.
Niches and Stem Cell Clonal Evolution
The replacement of a population by the progeny from a single cell is called clonal evolution, a term usually applied to tumor populations . The ability of stem cells occasionally to divide symmetrically also allows for the possibility that all stem cell lineages except one will eventually be lost from a niche (Fig. 8). Total stem cell numbers remain unchanged because extinction (two non-stem daughters) is balanced by expansion (two stem cell daughters). Stem cell niche clonal evolution is observed in the murine intestines because heterogeneously marked crypts become visibly homogeneous after several weeks to months, reflecting the loss with replacement of stem cell lineages. In murine intestines, symmetrical division occurs about 5% of the time .
Systematic visible cell fate marker experiments are not possible in humans, although heterogeneous appearing normal colon crypts tend to become homogeneous after therapeutic radiation . However, it is possible to infer if human intestinal crypt stem cells divide symmetrically by measuring crypt tag diversity. Daughter cells will tend to contain nearly identical 5′ to 3′ patterns, but eventually differences will accumulate (Fig. 3). Whereas mitotic age is a function of numbers of replication errors, the ancestry between tags may be summarized by methylation differences between their CpG sites. This pair-wise difference (Hamming distance) is zero for identical tags, and increases on average with more distantly related tags. Diverse cell populations have many different unique tags—“old populations are diverse populations.”
Diversity increases with division because new tag patterns may arise from new replication errors. Immortal stem cells that always divide asymmetrically result in tag diversity that tends to increase continuously because errors are never lost. By contrast, symmetrical divisions reduce diversity because unique tags are potentially eliminated when a stem cell produces two non-stem daughters. Niche clonal evolution creates population “bottlenecks” because stem cells share progressively more recent common ancestor (Fig. 8). Experimentally, human colon crypt tag diversity does not increase with chronological age, consistent with stem cell niches with occasional symmetric divisions . Similar to murine crypts, symmetric divisions may occur about 5% of the time in human crypts, resulting in periodic stem cell population “bottlenecks” or niche stem cell clonal evolution about every eight years .
The ability to reconstruct ancestry from replication errors illustrates how the life and death of stem cells may be inferred without experimental manipulations that may perturb normal behavior. Clonal evolution is not confined to tumor populations but appears to be a normal rhythm in niches, which may occur simply because it may be impossible for stem cells always to divide asymmetrically. Niches have the potential to act as evolutionary crucibles because only a limited number of progeny can remain within a niche. The dominant stem cell clone may arise by chance (or drift), but one stem cell may acquire a mutation that confers a selective advantage over the surrounding stem cells within the niche . Interestingly, certain dominant-negative APC mutations commonly found in colorectal cancers appear to enhance stem cell survival in normal appearing crypts . Given the loss of most stem cell lineages during niche clonal evolution , one potential explanation for some cancer mutations is “contingency”—the idea that transformation of a stem cell later in life is contingent on survival during earlier rounds of niche clonal evolution. Some of the genes mutated in cancers also appear important to stem cell survival, and alterations that enhance survival during niche clonal evolution could also increase the chances of transformation later in life.
Do Older Individuals Have “Older” Cancers?
Preliminary data illustrate that average colon cancer tag methylation is generally greater than corresponding normal tissue, and that tag methylation is generally greater in cancers removed from older individuals (Fig. 9b). Cancers in older individuals may have greater mitotic ages simply because the stem cell that transforms has a greater mitotic age in older individuals. Although further studies are necessary, this type of data suggests that cancer histories are also likely to be recorded by replication errors within their genomes. If cell division increases the risk for cancer , potentially more colon cancers arise in older individuals because colon stem cell mitotic ages normally increase with age (Fig. 9b).
Challenges and Problems
The reconstruction of histories from sequences remains controversial because the exact manner by which changes accumulate is uncertain, and the optimal methodologies to decode sequences are uncertain . The reconstruction of the past from genomes requires relatively sophisticated mathematical modeling to account for the stochastic nature of replication errors. Although conclusions based on a single locus are tenuous, the past becomes clearer when multiple genomic regions reconstruct the same ancestry .
Perhaps the greatest challenge is accepting that seemingly random methylation patterns (Fig. 5b) are “words” that retell stem cell ancestries. The exact mechanisms responsible for such methylation patterns are uncertain, but appropriate algorithms can reconstruct the past from random replication errors. These algorithms may be complex because methylation error rates may differ between tissues and sites within a tag. For example, for one clock tag, methylation at one CpG site appears to increase the probability of methylation at another CpG site .
A clock analysis is facilitated by the analysis of pure populations because genealogies likely differ between cell types. For example, bulk analysis of colon genomes is likely to yield nonsensical results because the mixtures of cells (epithelial, neutrophils, lymphocytes, smooth muscle, blood vessels) represent distinct and different mitotic histories. However, many tissues are composed of small clonal units such as colon crypts, and it is possible through microdissection or other techniques to isolate relatively pure cell populations. Although it is difficult to isolate and analyze genomes from individual cells, bisulfite sequencing of individually cloned PCR products allows for the sampling of individual tags from small clonal cell pools such as a 2,000 cell human colon crypt .
“Reading” the Past: The Language of Replication
Sequence comparison revolutionized species phylogeny reconstruction because it became possible to study the past from the present simply by sequencing and “reading” DNA . Similarly, it may be possible to characterize stem cells from the genomes of their progeny, without the paraphernalia traditionally associated with stem cell studies. Read one way, the 5′ to 3′ order of bases in a human genome retells the emergence of modern humans “out-of-Africa” about 50,000 years ago. Read another way, the 5′ to 3′ order of CpG methylation in a somatic cell genome potentially retells an emergence “out-of-embryo” decades ago.
The basic premise of translating molecular clocks from species to somatic cells is that even somatic cell genomes are not “created” but represent copies of copies. Evolution or change is possible because genomes are almost perfect rather than exact copies of prior genomes. Although certain methylation patterns may be spontaneously created or programmed, other patterns may simply represent outcomes of random replication errors, which potentially “chronicle” past divisions. The current approach with epigenetic molecular clocks may not be optimal, but the general strategy of using genomes to reconstruct ancestry can overcome many problems that currently hinder systematic studies of human stem cell biology. What yet unread stories await within our cells?