Research

Overview

The San Miguel Lab is dedicated to accelerating biological discoveries by combining engineering and systems approaches to answer elusive questions in different areas of biology. We use customized tools, such as microfluidics and computer vision, that enable performing imaging-based experiments in a high-throughput, high-content manner. We are interested in addressing fundamental questions regarding aging, stress, and neurodegeneration using the model organism C. elegans. We have expertise on high-content quantitative characterization of phenotypes (i.e., deep phenotyping) at various scales: from the subcellular level all the way to whole-organism behavioral outputs. Our main areas of focus and tools are detailed below. For information on our recent work, please check out our publications.

Focus Areas:

• Aging and Lifespan: C. elegans has served as an important system to discover important evolutionarily conserved pathways that modulate longevity. We are interested in understanding how these work together, and in identifying others we might still be missing. To address these questions, we are tracking the lifelong activity of key genes that regulate lifespan, by tagging the endogenous gene using CRISPR/Cas9 with a fluorescent marker. Quantitative analysis of spatiotemporal abundance of these genes throughout the animal’s lifespan enables linking molecular-scale information to lifespan, and thus reveals the contribution of each pathway to longevity. We are also using readouts that serve as a proxy for aging status to perform forward genetic screens on adults at a late reproductive state. This approach hinges on microfluidic platforms and automated image processing tools we have developed.
• Neurodegeneration and neuronal remodeling: Diverse stressors, including aging, result in neurodegenerative phenotypes in neurons, which exhibit neurite sprouting and disorganization, protrusions (beads, blebs, and bubbles), and neurite breaks. We are interested in determining how different stressors induce different morphological changes in neurons, and how these affect neuron function. For instance, some of these changes could be neuroprotective instead of degenerative. These phenotypes are, however, quite complex. To address this question, we rely on computer vision approaches (which hinge on machine learning tools) to extract information from images of degenerating neurons. We have found that aging and other stressors induce different patterns of beading, although these are seemingly identical to visual inspection. We are working on understanding the nature of these morphological defects, and their effect on neurotransmission and neuronal firing, using a combination of techniques including calcium imaging. We are also interested in using C. elegans as a model for Alzheimer’s Disease, and are developing new models that better recapitulate the biology of human disease, as well as tools to assess how stimuli (such as trauma) can modulate the disease.
• Stress response: Animals have evolved mechanisms to defend against environmental factors that induce stress and impact health or homeostasis. How multiple stressors or pathways interact is not fully understood. Likewise, although they seem to play a major role, it is unclear how neurons might control the stress response. We are studying how multiple stressors affect the organism and have found interesting synergistic and antagonistic interactions under diverse conditions. We use reporter strains to assess gene expression and a combination of on-chip and off-chip approaches for stress exposure and imaging. We are also studying how neurons regulate the stress response, and how the stress response can modulate neuronal activity.

Tools:

• Microfluidics: Some of our work heavily relies on the use and development of microfluidic approaches, which greatly facilitate animal handling and provide unprecedented control of the experimental interventions. Some examples include a worm sorting chip we use for high throughput genetic screening, a platform for culture and imaging of aging populations without the need for anesthetics or sterilizing drugs, a platform to study traumatic brain injury on chip, and a platform inspired in the Manta Ray feeding mechanism to passively filter aggregates or particles.
• Deep Phenotyping: Most of our work relies on quantitative analysis of image-based readouts. To take advantage of images, we extract a multitude of characteristics to build a phenotypic profile that can best describe the underlying biology. To do this, we rely on custom image processing algorithms and machine learning approaches, which allow segmenting complex images such as those from small structures (synapses) or neurodegenerating neurons.
• Genetic Engineering Approaches: We use molecular biology and transgenic approaches to generate strains with genetic modifications as reporter strains or disease models. We use established tools, such as extrachromosomal and integrated arrays, and precise modifications with CRISPR/Cas9.

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For our most recent completed work, please check out our publications!