Wide Field-of-view Microscopes and Endoscopes for Time-lapse Imaging and High-throughput Screening

Wide field-of-view (FOV) microscopy is of high importance to biological research and clinical diagnosis where a high-throughput screening of samples is needed. This thesis presents the development of several novel wide FOV imaging technologies and demonstrates their capabilities in longitudinal imaging of living organisms, on the scale of viral plaques to live cells and tissues.

The ePetri Dish is a wide FOV on-chip bright-field microscope. Here we applied an ePetri platform for plaque analysis of murine norovirus 1 (MNV-1). The ePetri offers the ability to dynamically track plaques at the individual cell death event level over a wide FOV of 6 mm × 4 mm at 30 min intervals. A density-based clustering algorithm is used to analyze the spatial-temporal distribution of cell death events to identify plaques at their earliest stages. We also demonstrate the capabilities of the ePetri in viral titer count and dynamically monitoring plaque formation, growth, and the influence of antiviral drugs.

We developed another wide FOV imaging technique, the Talbot microscope, for the fluorescence imaging of live cells. The Talbot microscope takes advantage of the Talbot effect and can generate a focal spot array to scan the fluorescence samples directly on-chip. It has a resolution of 1.2 μm and a FOV of ~13 mm². We further upgraded the Talbot microscope for the long-term time-lapse fluorescence imaging of live cell cultures, and analyzed the cells’ dynamic response to an anticancer drug.

We present two wide FOV endoscopes for tissue imaging, named the AnCam and the PanCam. The AnCam is based on the contact image sensor (CIS) technology, and can scan the whole anal canal within 10 seconds with a resolution of 89 μm, a maximum FOV of 100 mm × 120 mm, and a depth-of-field (DOF) of 0.65 mm. We also demonstrate the performance of the AnCam in whole anal canal imaging in both animal models and real patients. In addition to this, the PanCam is based on a smartphone platform integrated with a panoramic annular lens (PAL), and can capture a FOV of 18 mm × 120 mm in a single shot with a resolution of 100─140 μm. In this work we demonstrate the PanCam’s performance in imaging a stained tissue sample.

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.