The central nervous control of walking in the locust, Schistocerca Americana

Rhythmic motor behaviors in all animals appear to be under the control of "central pattern generator" circuits, neural circuits which can produce output patterns appropriate for behavior even when isolated from their normal peripheral inputs. Insects have been a useful model system in which to study the control of legged terrestrial locomotion. Much is known about walking in insects at the behavioral level, but to date there has been no clear demonstration that a central pattern generator for walking exists. The focus of this thesis is to explore the central neural basis for locomotion in the locust, Schistocerca americana.

Rhythmic motor patterns could be evoked in leg motor neurons of isolated thoracic ganglia of locusts by the muscarinic agonist pilocarpine. These motor patterns would be appropriate for the movement of single legs during walking. Rhythmic patterns could be evoked in all three thoracic ganglia, but the segmental rhythms differed in their sensitivities to pilocarpine, their frequencies, and the phase relationships of motor neuron antagonists. These different patterns could be generated by a simple adaptable model circuit, which was both simulated and implemented in VLSI hardware. The intersegmental coordination of leg motor rhythms was then examined in preparations of isolated chains of thoracic ganglia. Correlations between motor patterns in different thoracic ganglia indicated that central coupling between segmental pattern generators is likely to contribute to the coordination of the legs during walking.

The work described here clearly demonstrates that segmental pattern generators for walking exist in insects. The pattern generators produce motor outputs which are likely to contribute to the coordination of the joints of a limb, as well as the coordination of different limbs. These studies lay the groundwork for further studies to determine the relative contributions of central and sensory neural mechanisms to terrestrial walking.

Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.