Recent advances in understanding vertebrate segmentation

The Clock and Wavefront model

In 1976, a theoretical model was proposed by Cooke and Zeeman to explain the dynamics of somitogenesis—the Clock and Wavefront model²⁵. This model proposes that cells of the PSM present an internal molecular oscillator—the clock—which is paired with a wavefront of cell differentiation progressing caudally as the embryo body axis elongates. This first conceptual model provided a possible explanation for how the temporal dynamics of a clock could be translated into a spatial pattern of somites.

The molecular segmentation clock

Twenty years later, Palmeirim et al.²⁶ showed that c-hairy1, an avian homologue of the Drosophila hairy gene, was expressed dynamically in the PSM in a tissue-autonomous manner. Pulses of c-hairy1 occur in individual PSM cells, with a 90-minute period. The fact that the individual PSM cells along the A-P axis are in different phases of the gene expression cycle (reviewed in 27) creates a kinematic wave (a wave in which transport is absent or negligible) of gene expression that sweeps rostrally, arresting in the anterior PSM, correlating in time and space with somite boundary formation²⁶. These results provided the first evidence supporting the long-held clock and wavefront hypothesis²⁵.

Nowadays, the clock is regarded as a genetic network composed of cell-autonomous and cell-to-cell signaling components, spanning multiple cells. Most genes involved are from the Notch, Fgf, and Wnt signaling pathways^28,29 yet vary amongst model species such as chick, zebrafish, and mouse³⁰.

Time to space translation

The segmentation clock oscillates in time with a sinusoidal curve, whose phase is locally synchronized between neighboring cells. According to the Cooke and Zeeman²⁵ model, as the wavefront passes down the PSM, it interacts with the clock, causing cells within the same period of oscillation to differentiate and become part of the same segment. This interaction is proposed to activate a developmental program that yields the formation of an epithelial somite in the anterior PSM.

Figure 2. Time to space translation.

Kinematic waves of gene expression sweep the A-P axis, arresting anteriorly, followed by somite formation.

This theoretical framework has provided an explanation for how the embryo could translate the information encoded in the temporal periodicity of oscillations of individual cells onto a spatially periodic pattern of segmentation from head to tail along the PSM. This model presents two important predictions: (i) each segment length (S) is determined by the period of the clock (T) and the velocity of the wavefront’s progress across the PSM (v), i.e. S = v T, and (ii) the total number of segments in the embryo (n) is set by the total duration of the segmentation process (d) and the period of the clock (T), i.e. n = d/T³¹. This model places the period of the clock in a central role for determining the length and overall anatomy of the embryo^25,27,32. To form the correct number of somites, regardless of the differences in embryo size, both the period of the clock and the speed of the wavefront must be regulated in proportion to the overall length of the PSM²⁵.

Currently, new models contemplate the fact that the kinematic wave of clock expression narrows as it sweeps from the posterior to the anterior PSM, gradually slowing the clock anteriorly, with peaks of clock expression separated by one segment length in the anterior PSM^27,33–36.

Alternative models for segmentation

Three relevant alternative mechanisms to transform oscillations into spatial stripes have been proposed:

Most notably, in all of these models, the wavefront is an emergent property of the time-space transformation rather than a causal agent^16,40. Further experimental research is required to help define the models that provide the most insight into the segmentation mechanism.

Segment scaling

Lauschke and colleagues proposed in 2013 a segment scaling mechanism based on a Phase-Gradient encoding, i.e. the gradual shift in the oscillation phase of PSM cells encodes information for the spatial patterning of the PSM tissue³⁹. Contrary to long-range molecular gradients (such as the wavefront proposed by Cooke and Zeeman), a phase-gradient describes the distribution of a dynamic cellular state. This provides a possible explanation for how the embryo maintains stable segment proportions despite overall changes in size, e.g. due to normal embryo growth or as observed when, after experimental reduction of embryo size, the total number of segments remains unaltered and the segments become proportionally smaller^44,45.

Using their newly developed ex vivo experimental setting composed by a quasi-monolayer of mouse primary PSM cells (mPSM) combined with real-time gene expression imaging, the authors observed that (i) the period of oscillation in the central mPSM remains constant regardless of PSM length, (ii) the velocities of kinematic waves change linearly with overall mPSM length (larger samples display proportionally faster kinematic waves, indicating that oscillatory activity adapts to match the spatial context in which it occurs), and (iii) the phase-gradient slope is predictive of segment size.

Overall, this study suggests that segment size definition could be encoded at the level of phase differences between PSM cells, without the requirement for a molecular gradient, also known as a wavefront.

Period of segmentation

In 2014, Soroldoni and co-workers set out to study the period of segmentation. Using real-time measurements of genetic oscillations in zebrafish embryos, they showed that the time scale of genetic oscillations is not sufficient to explain the period of segmentation as the segmentation clock postulates⁴⁶. Instead, the rate of tissue shortening provides the second time scale necessary to determine the period of segmentation through what they termed a “Doppler effect” modulated by a gradual change in the oscillation profile across the tissue.

Briefly, they found that (i) both anterior and posterior PSM oscillate, albeit with different amplitudes, (ii) oscillations in the posterior PSM are slower than in the anterior PSM (i.e. there is not a single well-defined period for the segmentation clock), (iii) the period of anterior PSM oscillations matches the period of segmentation, and (iv) there is a substantial shortening of the PSM over time, leading to a relative motion of the anterior end of the PSM toward the posterior end (where the waves arrest), hence creating a "Doppler effect" due to the shortening of the time interval between wave onset and arrest⁴⁶.

Overall, the authors conclude that the rhythm of sequential segmentation is an emergent property controlled by three variables: the period of the individual genetic oscillators, the change of the kinematic wavelength (i.e. oscillation profile), and the shortening of the PSM length. Notably, the authors remark that such a “Doppler effect” is incompatible with the previously proposed Phase-Gradient scaling mechanism, since the latter requires that the number of waves along the PSM remains constant, whereas this study shows that, at least in zebrafish, the number of waves changes between anterior and posterior PSM.

Segmentation clock dynamics

In 2015, Shih et al.³⁶ studied, in vivo, the dynamics of the clock slowing down relative to somite boundary formation as it moves anteriorly through the PSM³⁶. For this, they followed the PSM segmentation clock oscillations in real-time, with single-cell resolution, in zebrafish embryos.

By focusing on cells that eventually form each somite boundary, they showed that (i) in vivo, the clock gradually slows down in the more anterior PSM cells, creating a distribution of oscillation phases—a spatial and temporal phase-gradient—where cells at one-somite distance are in opposite phases of clock expression, (ii) the clock wave increases in amplitude for the final two oscillations, and (iii) PSM cells oscillate until they incorporate into somites.

Most importantly, the authors discuss the lack of experimental evidence for the theoretical “arrest-front” causing oscillations to smoothly stop in the anterior PSM by the posteriorly progressing wavefront postulated by the Clock and Wavefront model²⁵. Instead, they propose the Clock Wave Stopping model. This proposal postulates that the segmentation clock itself may play a role in determining the wavefront by creating a signal directly encoded by the phase-gradient, with a two-somite periodicity³⁶. This is in line with the suggestion from Lauschke et al.³⁹.

This model is based on one important observation: within a given forming somite, individual cells stop oscillating in discrete groups in a posterior to anterior direction (P-A), i.e. in the same direction as the waves of clock expression and not in the opposite direction, as would be expected from an A-P wavefront stopping signal. In this Clock Wave Stopping model, cells in the anterior PSM continue to oscillate with their neighbors, regardless of future somite position, consistent with the observations that synchrony is regulated by Delta-Notch interactions^47,48.

Autonomous segmentation clock

In early 2016, Webb et al.³¹ addressed the longstanding question of whether individual PSM cells are able to sustain autonomous oscillations without the external input from neighboring cells.

To this end, the authors recorded the expression of Her1 in single cells from a transgenic reporter cell line obtained from the tailbud of zebrafish, showing that the individual cells are capable of autonomous genetic oscillations in vitro, in the absence of intercellular communication, albeit with substantial variability (noise)³¹. When comparing these autonomous oscillator data with the oscillations at the tissue level in intact zebrafish embryos (from Soroldoni et al.⁴⁶), the authors concluded that individual cells have a longer period (ca. twofold slower) and are less precise than the population at the tissue level. This shows the importance of synchronization and coupling for the proper working of the segmentation clock.

Self-organized spatiotemporal waves

Also in 2016, Tsiairis and Aulehla⁴⁹ presented compelling evidence that PSM cells can self-organize from disordered initial conditions. For this study, the authors dissociated the PSM from several mouse embryos into single cells and used the randomized cell suspension to generate dense cell re-aggregates. These were then cultured and subjected to real-time imaging and quantification of signaling activity using the dynamic Notch signaling reporter LuVeLu (Venus fluorescence driven from the lunatic fringe promoter)⁴⁹.

They reported several relevant findings, from which we highlight the following: (i) after 5–6 hours of culture, cells synchronize and exhibit in-phase oscillations in multiple foci that formed within each re-aggregate, (ii) self-organized foci recapitulate spatiotemporal organization of in vivo PSM, indicating that each focus represents miniature PSM (emerging PSM) that forms spontaneously upon randomization and re-aggregation, (iii) the collective synchronization of the cells corresponds to the arithmetic average of the input oscillator frequencies (i.e. it depends on frequencies of the input cell population), as predicted in models for coupled phase oscillators⁵⁰, rather than matching the highest frequency of potential pacemaker cells, and (iv) upon DAPT treatment (a chemical inhibitor of Notch signaling), randomized PSM cells fail to synchronize, in agreement with previous in vivo findings⁵¹; however, the individual cells maintained oscillatory activities with amplitudes similar to the untreated ones.

Overall, this report indicates that, after randomization, individual mouse PSM cells self-organize into ordered macroscopic miniature PSM structures that are capable of tuning oscillation dynamics in response to surrounding cells, leading to collective synchronization with an average frequency, re-establishing wave-like patterns of gene activity.

Control of the segmentation clock period

Later in the same year, Liao et al.⁵² asked whether an increase in Delta-Notch signaling causes a change in the segmentation period. To answer this, they generated transgenic zebrafish lines with a range of extra copies of a transgene containing the deltaD locus (together with its full genomic regulatory region) and measured the segmentation period by multiple embryo time-lapse microscopy⁵².

They found that only the highest level of DeltaD expression (in the Damascus zebrafish line containing ca. 100 copies of deltaD-venus transgene) produces altered oscillating gene expression wave patterns and shorter segmentation periods (6.5% faster), generating embryos with more, and shorter, body segments. Moreover, the effect on period was lost by incubating Damascus embryos with DAPT (Notch intracellular domain blocker), showing that the observed phenotype is indeed modulated by the overexpression of Delta-Notch molecular players.

The contribution of the proposed "Doppler effect"⁴⁶ on the observed change in segmentation period was also evaluated, showing that the alteration of the wave pattern, and not changes in the rate of tissue shortening, is responsible for the majority of the observed period difference.

Finally, the authors put forward a simplified model of coupled oscillators with time delay to explain the shorter segmentation period. Briefly, the coupling strength (i.e. the number of activated ligand–receptor pairs over time) is expected to increase as a consequence of the over-expression of DeltaD, and the time delay in the coupling is not negligible (i.e., it is significant the time taken to transfer the signal from one cell to another owing to the time required to synthesize and traffic Delta proteins). The numerical simulation of such parameter changes leads to a stable time-periodic wave pattern with an increased number of waves and a shortened anterior wavelength, as experimentally observed in the Damascus line.

Thus, this study hints that the Delta-Notch signaling, besides synchronizing oscillators, seems to alter the segmentation period by affecting the wavelength of the tissue’s spatial pattern. However, this issue is not easy to address, since several alternative outcomes arise from changing the Delta-Notch signaling. For instance, a lengthening of the period due to a reduction in Delta-Notch signaling is observed in zebrafish. However, mouse segmentation is faster with the blockade of Notch, an outcome predicted only if the length of the time delay in the coupling was shorter than half the period^34,53.

Cellular oscillator

Q1 | Recently, it has been shown in zebrafish that single cells carrying a her1-yfp transgene can oscillate autonomously³¹. However, it remains unknown if this is a particular feature of this organism, or if it is an intrinsic characteristic of the segmentation clock cells and hence observed in other species (this has been attempted in chick⁵⁴ and mouse⁵⁵ but without definite conclusions owing to the technological limitations at the time).

Local synchrony

Q2 | Wave patterns arising from individual oscillators require the establishment of a spatial profile in their phases. How are these wave patterns regulated?

Q3 | The Delta-Notch intercellular signaling pathway is associated with multiple processes, namely lateral inhibition, border formation, and local synchronization. How is the Delta-Notch signaling accomplishing these multiple outcomes?

Global control

Q4 | In vertebrates, the wavefront has been shown to be influenced by gradients of Wnt and Fgf signaling coming from the caudal end and a counter-gradient of retinoic acid (RA) from the somites. How do oscillating cells use and interpret these signaling gradients as stop signals to arrest the oscillation?

Q5 | In vertebrates, embryo segmentation occurs simultaneously with body elongation. Is it possible to disentangle these two processes in order to study them separately?

Q6 | The first five somites are not sequentially formed like the ones from the trunk⁴³. Instead, they form almost simultaneously and without cyclical expression of "clock genes"⁵⁶. Is there an alternative mechanism responsible for occipital somite formation?

Q7 | Recently, relevant challenges have been made to the clock and wavefront model. Particularly important is the fact that, in order to fully grasp the global mechanism underlying segmentation, it is necessary not only to study gene expression dynamics but also to integrate cellular processes like division, movement, and differentiation⁵⁷. Such a comprehensive theoretical framework would generate an invaluable tool to create new hypotheses and test old ones.