1,721,043 research outputs found
Unsupervised Quantification of Stereotyped Behavior in Flies and Mice
No full textRecent advances in the study of animal behavior have utilized tools from computer vision and machine learning in order to perform high-throughput behavior quantification. Here, we utilize a behavior analysis paradigm established by Berman et al. (2014). Using a principle component-based approach, raw behavior video data is reduced to a postural time series. A dimensionality reduction approach embeds the dynamics of this time series into a two-dimensional “behavioral map,” and discrete behaviors are identified as peaks in the map's probability density function.
Here, we expand on the work of Berman et al. in two main ways. Firstly, we define a metric for quantifying the stereotypy of a given behavior, based on the variance in a behavior's postural trajectory over many instances of the behavior. We find that most identified behaviors in the fruit fly Drosophila exhibit a high degree of stereotypy, providing support for the view of behavior as a set of discrete states, at least to an approximation.
Additionally, we apply this framework, which was originally developed for Drosophila, to the mouse. The mouse is a prominent model organism in neuroscience research, and we hope that these behavior quantification techniques can be combined with modern neural recording and perturbation techniques in order to understand the mechanisms through which the brain responds to external stimuli and generates complex behaviors. Finally, we utilize proof-of-concept experiments to suggest that our analyses can quantify the behavioral effect of neuropharmacological agents and neural perturbations with optogenetics
Behavior at High Resolution
No full textThe physics of animal behavior is at an exciting time where an explosion of new computational tools and techniques is giving us access to unprecedented amounts of high quality data. With these superior data, we can probe long standing questions in the physics behavior about the temporal structure of behavior, the impact of biological factors on behavior, and how internal state and external context modulate behavior. Here I present two projects that take advantage of the opportunities created by these advances to examine behavior at high resolution.
An ongoing area of interest in the physics of behavior is the temporal structure of animal behavior. Progress has been limited by the availability of long timescale data sets. We set out to provide a new high resolution \textit{Drosophila melanogaster} data set as a resource to the field of the physics of behavior. We developed a methodology to capture and process high resolution, high frame rate, continuous recordings of freely behaving \textit{Drosophila melanogaster} over the course of 4-7 days of life. Our data captures well known behavior effects, such as habituation to new environments and circadian rhythms of locomotion. This data set will provide a foundation upon which the field can build our next generation of computational and analytical advances.
We also leverage the tools of computational ethology to quantify and study bumblebee social behaviors. We perturbed the early life social environment of \textit{Bombus impatiens} by isolating them from their natal colony during a critical brain development period. We then assayed their behaviors both alone and in pairs. We found that isolation altered behavioral biases in solo social contexts. We also found significant perturbations to social behaviors, including altered reactions to proximity with a social partner and a loss of specificity in antennation behaviors. This, in combination with the neurogenomic effects found by our collaborators, shows that early life social isolation has a significant effect on neurogenomic development and later solo and social behaviors
Multimodal behavioral aging profiles in Drosophila models of human neurodegenerative diseases
No full textNeurodegenerative diseases are highly disruptive, and they play critical roles in end of life
behavior. Much of the literature on neurodegenerative disease focuses on biochemical and
physiological decline with age, but fails to rigorously categorize the vast set of behavioral
changes that occur. In order to gain a more complete understanding of neurodegeneration,
we use a data-driven approach to construct a space that describes the complete set of
behaviors demonstrated by Drosophila melanogaster. By examining differences in the types
of behaviors and rate of aging in three neurodegenerative models, we observe non-monotonic
aging profiles that differ across the mutants. Closer examination of individual behaviors
in these profiles indicates that behaviors do not necessarily covary with age, nor are they
affected by each neurodegenerative disease in the same manner. This finding emphasizes
the importance of studying nuanced behavioral change at a more granular level, since
neurodegeneration gives rise to non-monotonic behavioral aging profiles that show distinct
modular patterns
The mechanical basis of Myxococcus xanthus self-organization and motility: from single cells to collective behavior
No full textMany living organisms exhibit complicated but highly organized collective behaviors. Unlike passive systems whose dynamics are driven by thermal energy, living organisms are often active systems, in which motilities of individuals put the system far away from equilibrium. Naturally living in soil, social bacteria Myxococcus xanthus exhibits a series of fascinating self-organizing behaviors throughout its developmental cycle, including swarming during vegetative growth, forming rippling waves during predation, and aggregating into fruiting bodies when starved. In this thesis, I use M. xanthus as a model system and explore the mechanical basis behind its motility and self-organization. My work emphasizes on the utilization of microscopy, image processing techniques and data-driven analysis in the examination of M. xanthus on different scales. We first study M. xanthus fruiting body formation based on the statistical physics of active populations. We show that the aggregation process in M. xanthus resembles the dynamics of a spinodal decomposition phase separation. Modeling M. xanthus as active brownian particles, we demonstrate that the phase separation can be understood in terms of cell density and the Peclet number that captures the cell motility regarding its speed and reversal frequency. M. xanthus cells actively take advantage of their cellular control of motility to drive large scale aggregation by promoting gliding speed and suppressing reversals to increase motility persistence. Then we characterize the rippling behavior of M. xanthus both on single cell motility level and on population level. We find tracking data of single cells during rippling lacks the evidence to support the cell motion synchronization hypothesis. Using two different image processing techniques, we are able to characterize low density rippling structures by estimating local cell density. We further examine the high density rippling wave structures using a 3D laser microscope. Finally, we study the force generation mechanism of two distinct motility systems in M. xanthus. We find that cells use these motility systems in coordination while in groups. These results not only provided further understandings of the scale of forces M. xanthus experiences and exerts, but also suggests that M. xanthus mainly utilizes gliding motility during group migration
Exploring the dynamic 3D world inside bacteria with advanced optical microscopy
No full textThe bacterial cytoplasm is extremely crowded and polydisperse, with biomolecules spanning many orders of magnitude in size and electrical charge. In Chapter 2, we study this environment by reconstructing the 3D motion of size and charge-controlled nanoparticles (GEMs) with nanometer-scale resolution in live Escherichia coli cells. GEMs range in size from 20 to 50 nm and in charge from -2160 to 1800 e, similar to important macromolecules. In collaboration with theorists at Stanford, we show that GEMs spatially segregate by size and charge due to cytoplasmic polydispersity and charge interactions with cellular components.
Using biplane microscopy for 3D tracking, Monte Carlo simulations, and full-cell colloidal simulations by our collaborators, we find that regardless of charge and particle size, the motion in bacterial cells is mostly normally diffusive. However, this motion appears sub-diffusive due to geometrical confinement to the small cellular volume.
We make progress towards understanding the role of chromosome remodeling on particle diffusion in bacterial cells. We show that 50 nm GEMs in exponentially growing cells experience caging, inhibiting the motion of a fraction of the particles. This caging does not occur for smaller GEMs in exponential phase or for 50 nm GEMs in stationary phase cells, suggesting that caging stems from the DNA pore size and active chromosomal re-arrangements.
Most of this thesis focuses on freely diffusing particles. However, there is a wealth of interesting phenomena occurring in tethered systems, where particle localization is determined by the anchoring of the molecule, and both the local environment and the tethering action influence dynamics. This is the case for chromosomal loci. In Chapter 4, I conclude by describing steps we have taken towards studying the physics of the bacterial chromosome and propose experiments to study this system using the techniques developed in Chapter 2.
In Chapter 3, I present our work rebuilding and optimizing a Stimulated Emission Depletion super resolution microscope. This technique enables sub-diffraction limit imaging by using optical patterning to selectively de-activate fluorescent probes during image acquisition. Although modest super-resolution was achieved, the microscope was never used for biological imaging due to its optical and biological limitations
The Role of MreB in E. coli Shape Determination and Whole-Brain Calcium Dynamics in Freely Behaving C. elegans
No full textBacteria have remarkably robust cell shape control mechanisms, but the mechanisms of self-organization for robust morphological maintenance remain unclear in most systems. Precise regulation of rod shape in Escherichia coli cells requires the MreB actin-like cytoskeleton, but the mechanism by which MreB maintains rod shape and sets the cell diameter is unknown. Here, I study both of these mechanisms using a novel method for extracting the 3D shape of cells using fluorescence image stacks and forward convolution. I then use time-lapse and 3D imaging coupled with computational analysis to map the growth, geometry, and cytoskeletal organization of single bacterial. Our results demonstrate that feedback between cell geometry and MreB localization maintains rod-like cell shape by targeting cell wall growth to regions of negative cell wall curvature.
I also study how MreB sets the diameter of a cell. Here, I perturb MreB by treating cells with the drug A22 or by creating mreb point mutants. These perturbations modify the steady state diameter of cells to between 790±30 nm to 1700±20 nm. I correlated structural characteristics of fluorescently-tagged MreB polymers to cell diameter and show that the helical pitch angle of MreB inversely correlates with the cell diameter of E. coli. These results demonstrate that the physical properties of MreB filaments are important for shape control and support a model in which MreB organizes the cell wall growth machinery to produce a chiral cell wall structure and dictates cell diameter.
After investigating the control of bacterial cell shape, I study an interesting problem in neuroscience. The ability to acquire large-scale recordings of neuronal activity in freely behaving animals is needed to provide new insights into how populations of neurons generate behavior. I present a new instrument capable of recording intracellular calcium transients from the majority of neurons in the head of a freely behaving Caenorhabditis elegans with cellular resolution while simultaneously recording the animal's behavior. We observe calcium transients from 89 neurons for nearly four minutes and correlate this activity with the animal's behavior. We show that, across worms, multiple neurons show significant correlations with, backward, and turning locomotion
Statistical Analysis of Transcription Locations in Early Drosophila Development
No full textNew technologies have allowed for precise measurements of transcription
locations in living Drosophila embryos. The ability to track transcription
as it happens means that new avenues of exploration become possible.
One of these avenues is tracking the random motion of these transcription
locations. This paper covers the theoretical background behind random
walks and transcription and ultimately analyzes transcription locations in
order to create a model for their movement
Cell Wall Nonlinear Elasticity and Growth Dynamics: How Do Bacterial Cells Regulate Pressure and Growth?
No full textIn my thesis, I study intact and bulging Escherichia coli cells using atomic force microscopy to separate the contributions of the cell wall and turgor pressure to the overall cell stiffness. I find strong evidence of power--law stress--stiffening in the E. coli cell wall, with an exponent of 1.22±0.12, such that the wall is significantly stiffer in intact cells (E = 23±8 MPa and 49±20 MPa in the axial and circumferential directions) than in unpressurized sacculi. These measurements also indicate that the turgor pressure in living cells E. coli is 29±3 kPa. The nonlinearity in cell elasticity serves as a plausible mechanism to balance the mechanical protection and tension measurement sensitivity of the cell envelope. I also study the growth dynamics of the Bacillus subtilis cell wall to help understand the mechanism of the spatiotemporal order of inserting new cell wall material. High density fluorescent markers are used to label the entire cell surface to capture the morphological changes of the cell surface at sub-cellular to diffraction-limited spatial resolution and sub-minute temporal resolution. This approach reveals that rod-shaped chaining B. subtilis cells grow and twist in a highly heterogeneous fashion both spatially and temporally. Regions of high growth and twisting activity have a typical length scale of 5 μm, and last for 10-40 minutes.
Motivated by the quantification of the cell wall growth dynamics, two microscopy and image analysis techniques are developed and applied to broader applications beyond resolving bacterial growth. To resolve densely distributed quantum dots, we present a fast and efficient image analysis algorithm, namely Spatial Covariance Reconstruction (SCORE) microscopy that takes into account the blinking statistics of the fluorescence emitters. We achieve sub-diffraction lateral resolution of 100 nm from 5 to 7 seconds of imaging, which is at least an order of magnitude faster than single-particle localization based methods such as STORM and PALM. SCORE is insensitive to background and can be applied to different types of fluorescence sources, including but not limited to organic dye and quantum dot that are tested experimentally in this thesis. The second development is an extension from tracking single quantum dot to the more general cases of moving objects at high density based on active contour model. I add a repulsive interaction between open contours to the original model and treat the trajectories as extrusions in the temporal dimension. This technique is applicable to a broad range of problems and two specific tracking problems are chosen as illustrations: (i) the quantification of walking and chasing behaviors of Drosophila and (ii) the study of trajectories of gliding bacteria Myxococcus xanthus on flat surface. I demonstrate the capability of this high-through and highly automated analysis method for studying social and group behaviors in interacting organisms
Circadian Rhythmicity and Timescales in Fly Behavior
No full textA quantitative, data-driven assay for classification of behavior in Drosophila melanogaster
is applied to day-long imaging datasets to understand the interaction of behavioral
timescales over six orders of magnitude, particularly the longest circadian timescale.
We show evidence that circadian behavioral modulation exists beyond binary measurements
of locomotor activity or inactivity accessible to previous studies. In particular,
peaks of activity for different behaviors occur out of phase over the course
of the day. Predictability of behavior varies over the course of the day, as do the
relative importance of different timescales in explaining behavioral variability. These
changes are observed with and without extrinsic circadian cues. We investigate short
timescale interactions between behaviors underlying these larger trends. A perspective
is developed toward using these analyses to understand circadian regulation of
behavioral outputs
Quantifying the Behavioral Response to Optogenetic Stimulation of Mechanosensory Neurons in C. elegans
No full textThis thesis looks at the relationship between the neural activity and the behavior
of a freely moving roundworm Caenorhabditis elegans. We optically activate two
mechanosensory neurons in vivo and use advanced image and statistical analysis to
quantify the behavior, and consequently the behavioral changes that arise due to the
perturbation of the neurvous system. We outline the procedure to measure and identify
behaviors in a lower dimensional space, which allows us to explain the consequences of
the activity of specific neurons, and to potentially use this analysis to further investigate
functional structure of the animal nervous system.
Despite technological advances, the ways in which animal brains function remain
elusive due to the complexity of most animal nervous systems. The structure of most
brains is also rarely well known. Our model organism has one of the best researched
nervous systems in the animal kingdom. C. elegans is a transparent nematode whose
brain structure has been studied in detail [29] and all of its 302 neurons and their
connections have been identified. Yet, we still have only a limited knowledge of the
neural dynamics and functions that specific neurons perform.
One of the most interesting scientific questions today is how does the brain activity
correlate with the behavior. Many experiments have been done in this field, like that
of Sawin [21], but so far they have relied on subjective categorizations of behavior. We
employ dimensionality reduction by Principal Component Analysis to objectively and
mathematically categorize the most important components of animal behavior robustly
[23, 25, 26, 27].
We find that the laser stimulation of the OLQ and CEP neuron alters the values
of behavioral components we measure in a complex way, illustrating a non-trivial relationship
between the activity of these two neurons and the motor activity of C. elegans.
Moreover, we identify the behavior that the optogenetic stimulation of CEP neuron
causes. Our ability to reconstruct the exact behavioral response thus draws a strong
quantitative connection between the brain activity and behavior of C. elegans
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