Khaled Khairy - Academia.edu (original) (raw)
Papers by Khaled Khairy
Supplementary Table from Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation
Supplementary Data from Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation
Clinical Lymphoma Myeloma and Leukemia
Frontiers in Bioinformatics
Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional... more Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI ...
Frontiers in Bioinformatics
Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (L... more Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated wit...
Biophysical Journal, 2022
The neural circuits responsible for animal behavior remain largely unknown. We summarize new meth... more The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the ev...
ArXiv, 2018
Large electron microscopy image datasets for connectomics are typically composed of thousands to ... more Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registra...
Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biolog... more Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 108 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render [18] services used in the volume assembly of ...
Mechanics plays a key role in the development of higher organisms. However, understanding the rol... more Mechanics plays a key role in the development of higher organisms. However, understanding the role of mechanics is complicated by the fact that it has proven difficult to model the link between local forces generated at the subcellular level, and tissue deformation at the whole-embryo level. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation at the cytoskeletal level only enters the shape mechanics calculation in the form of local changes in preferred tissue curvature. This allows us to formulate the continuum mechanics problem purely in terms of tissue strains. Relaxing the system by lowering its mechanical energy yields global morphogenetic predictions that accommodate the tendency towards this local preferred curvature, without explicitly modeling molecular-scale force-generating mechanisms. Our computational framework, which we call SPHARM-MECH, extends a three-dimensional spherical harmonics parameterization known as SPH...
The neural circuits responsible for animal behavior remain largely unknown. We summarize new meth... more The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the ev...
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
Recent advancements in microscopy, protein engineering, and genetics have rendered the larval zer... more Recent advancements in microscopy, protein engineering, and genetics have rendered the larval zerbrafish a powerful model system for which whole brain, real time, functional neuroimaging at cellular resolution is accessible. Supplementing functional data with additional modalities in the same fish such as structural connectivity and transcriptomics will enable interpretation of structure-function relationships across the entire brains of individual animals. However, proper identification of corresponding cells in the large image volumes produced depends on accurate and efficient deformable registration. To address this challenge, we implemented the Fourier-approximated Lie Algebras for Shooting (FLASH) algorithm within the well-known Advanced Normalization Tools (ANTs) package. This combines the speed of FLASH with the extensive set of image matching functionals and multi-staging multi-resolution capabilities of ANTs. We registered longitudinal data from nine fish, using a line that...
A comparison between the full Newton-type optimization NL2SNO, the Levenberg–Marquardt method wit... more A comparison between the full Newton-type optimization NL2SNO, the Levenberg–Marquardt method with the model-trust region modification, and the simplex algorithm is made in the context of the iterative fitting of EPR spectra. EPR lineshape simulations are based on the stochastic Liouville equation (SLE), with an anisotropic diffusion tensor and an anisotropic restraining potential describing the motional amplitude of the spin label. The simplex algorithm was found to be the most reliable, and an approach—incorporating both NL2SNO as well as the downhill simplex methods—is proposed as a strategy-of-choice.
Biophysical Journal
Mechanics plays a key role in the development of higher organisms. However, understanding this re... more Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system's mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.
Nature Methods
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and ph... more Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data. Recent developments in technology, such as high-throughput imaging and sequencing, enable experimentalists to collect increasingly large, complex, and heterogeneous "big" data [1]. Any study includes both raw data and metadata, potentially resulting in terabytes per day, eventually yielding petabytes when aggregating across experiments and laboratories. These new experimental capabilities exceed the scale or feature-set of existing software. For example, such data cannot be stored, processed, and visualized on a laptop or workstation. Instead, big data require complex registration, processing, and machine learning pipelines, to be stored on data centers and processed on high-performance and/or cluster computers.
Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron... more Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly. We validated the dataset by tracing brain-spanning circuitry involving the mushroom body (MB), intensively studied for its role in learning. Here we describe the complete set of olfactory inputs to the MB; find a new cell type providing driving input to Kenyon cells (the intrinsic MB neurons); identify neurons postsynaptic to Kenyon cell dendrites; and find that axonal arbors providing input to the MB calyx are more tightly clustered than previously indicated by light-level data. This freely available EM dataset will significantly accelerate Drosophila neuroscience.
Active contours is a powerful image segmentation technique based on simultaneously optimizing the... more Active contours is a powerful image segmentation technique based on simultaneously optimizing the overlap of a surface contour with the intensity image (external energy) on the one hand, and a constraining image-independent penalty based on the first and second derivatives of the contour (internal energy) on the other. Although the above form is applicable to a wide class of images, including prior information about the topology and smoothness as well as insights from physical theories regarding specific material properties of the object under study are expected to result in faster and more accurate segmentations. In this work we extend the formulation of the active contour internal energy for the common case of 3D-imaging lipid-bilayer membranebound objects of topological genus zero. Examples include organelles, cells and artificial vesicles. In the non-supervised method presented here, the internal energy takes into account membrane bending elasticity as well as constraints imposed by the fact that the two bilayer leaflets are allowed to slide relative to each other. An additional topology constraint is implicitly accounted for by using a spherical harmonics parametric contour representation. The balance between internal and external energies (i.e. the regularization parameter) is determined using the L-curve method. To ensure convergence and numerical stability a good starting guess for the contour is essential. We show in detail a method, that also makes use of the L-curve, for calculating this guess, and apply the complete procedure to a representative synthetic data set using realistic physical quantities based on membrane biophysical theories and known experimental results.
Mechanics plays a key role in the development of higher organisms. However, working towards an un... more Mechanics plays a key role in the development of higher organisms. However, working towards an understanding of this relationship is complicated by the fact that it has proven difficult to model the link between local forces generated at the subcellular level, and tissue deformation at the whole-embryo level. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation at the cytoskeletal level only enters the shape mechanics calculation in the form of local changes in preferred tissue curvature. This allows us to formulate the continuum mechanics problem purely in terms of tissue strains. Relaxing the system by lowering its mechanical energy yields global morphogenetic predictions that accommodate the tendency towards this local preferred curvature, without explicitly modeling force-generating mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a three-dimensional spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions, on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.
Methods in Molecular Biology, 2012
Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for ... more Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for the life sciences. LSFM provides an exceptionally high imaging speed, high signal-to-noise ratio, low level of photo-bleaching, and good optical penetration depth. This unique combination of capabilities makes light sheet-based microscopes highly suitable for live imaging applications. Here, we provide an overview of light sheet-based microscopy assays for in vitro and in vivo imaging of biological samples, including cell extracts, soft gels, and large multicellular organisms. We furthermore describe computational tools for basic image processing and data inspection.
Supplementary Table from Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation
Supplementary Data from Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation
Clinical Lymphoma Myeloma and Leukemia
Frontiers in Bioinformatics
Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional... more Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI ...
Frontiers in Bioinformatics
Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (L... more Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated wit...
Biophysical Journal, 2022
The neural circuits responsible for animal behavior remain largely unknown. We summarize new meth... more The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the ev...
ArXiv, 2018
Large electron microscopy image datasets for connectomics are typically composed of thousands to ... more Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registra...
Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biolog... more Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 108 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render [18] services used in the volume assembly of ...
Mechanics plays a key role in the development of higher organisms. However, understanding the rol... more Mechanics plays a key role in the development of higher organisms. However, understanding the role of mechanics is complicated by the fact that it has proven difficult to model the link between local forces generated at the subcellular level, and tissue deformation at the whole-embryo level. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation at the cytoskeletal level only enters the shape mechanics calculation in the form of local changes in preferred tissue curvature. This allows us to formulate the continuum mechanics problem purely in terms of tissue strains. Relaxing the system by lowering its mechanical energy yields global morphogenetic predictions that accommodate the tendency towards this local preferred curvature, without explicitly modeling molecular-scale force-generating mechanisms. Our computational framework, which we call SPHARM-MECH, extends a three-dimensional spherical harmonics parameterization known as SPH...
The neural circuits responsible for animal behavior remain largely unknown. We summarize new meth... more The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the ev...
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
Recent advancements in microscopy, protein engineering, and genetics have rendered the larval zer... more Recent advancements in microscopy, protein engineering, and genetics have rendered the larval zerbrafish a powerful model system for which whole brain, real time, functional neuroimaging at cellular resolution is accessible. Supplementing functional data with additional modalities in the same fish such as structural connectivity and transcriptomics will enable interpretation of structure-function relationships across the entire brains of individual animals. However, proper identification of corresponding cells in the large image volumes produced depends on accurate and efficient deformable registration. To address this challenge, we implemented the Fourier-approximated Lie Algebras for Shooting (FLASH) algorithm within the well-known Advanced Normalization Tools (ANTs) package. This combines the speed of FLASH with the extensive set of image matching functionals and multi-staging multi-resolution capabilities of ANTs. We registered longitudinal data from nine fish, using a line that...
A comparison between the full Newton-type optimization NL2SNO, the Levenberg–Marquardt method wit... more A comparison between the full Newton-type optimization NL2SNO, the Levenberg–Marquardt method with the model-trust region modification, and the simplex algorithm is made in the context of the iterative fitting of EPR spectra. EPR lineshape simulations are based on the stochastic Liouville equation (SLE), with an anisotropic diffusion tensor and an anisotropic restraining potential describing the motional amplitude of the spin label. The simplex algorithm was found to be the most reliable, and an approach—incorporating both NL2SNO as well as the downhill simplex methods—is proposed as a strategy-of-choice.
Biophysical Journal
Mechanics plays a key role in the development of higher organisms. However, understanding this re... more Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system's mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.
Nature Methods
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and ph... more Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data. Recent developments in technology, such as high-throughput imaging and sequencing, enable experimentalists to collect increasingly large, complex, and heterogeneous "big" data [1]. Any study includes both raw data and metadata, potentially resulting in terabytes per day, eventually yielding petabytes when aggregating across experiments and laboratories. These new experimental capabilities exceed the scale or feature-set of existing software. For example, such data cannot be stored, processed, and visualized on a laptop or workstation. Instead, big data require complex registration, processing, and machine learning pipelines, to be stored on data centers and processed on high-performance and/or cluster computers.
Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron... more Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly. We validated the dataset by tracing brain-spanning circuitry involving the mushroom body (MB), intensively studied for its role in learning. Here we describe the complete set of olfactory inputs to the MB; find a new cell type providing driving input to Kenyon cells (the intrinsic MB neurons); identify neurons postsynaptic to Kenyon cell dendrites; and find that axonal arbors providing input to the MB calyx are more tightly clustered than previously indicated by light-level data. This freely available EM dataset will significantly accelerate Drosophila neuroscience.
Active contours is a powerful image segmentation technique based on simultaneously optimizing the... more Active contours is a powerful image segmentation technique based on simultaneously optimizing the overlap of a surface contour with the intensity image (external energy) on the one hand, and a constraining image-independent penalty based on the first and second derivatives of the contour (internal energy) on the other. Although the above form is applicable to a wide class of images, including prior information about the topology and smoothness as well as insights from physical theories regarding specific material properties of the object under study are expected to result in faster and more accurate segmentations. In this work we extend the formulation of the active contour internal energy for the common case of 3D-imaging lipid-bilayer membranebound objects of topological genus zero. Examples include organelles, cells and artificial vesicles. In the non-supervised method presented here, the internal energy takes into account membrane bending elasticity as well as constraints imposed by the fact that the two bilayer leaflets are allowed to slide relative to each other. An additional topology constraint is implicitly accounted for by using a spherical harmonics parametric contour representation. The balance between internal and external energies (i.e. the regularization parameter) is determined using the L-curve method. To ensure convergence and numerical stability a good starting guess for the contour is essential. We show in detail a method, that also makes use of the L-curve, for calculating this guess, and apply the complete procedure to a representative synthetic data set using realistic physical quantities based on membrane biophysical theories and known experimental results.
Mechanics plays a key role in the development of higher organisms. However, working towards an un... more Mechanics plays a key role in the development of higher organisms. However, working towards an understanding of this relationship is complicated by the fact that it has proven difficult to model the link between local forces generated at the subcellular level, and tissue deformation at the whole-embryo level. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation at the cytoskeletal level only enters the shape mechanics calculation in the form of local changes in preferred tissue curvature. This allows us to formulate the continuum mechanics problem purely in terms of tissue strains. Relaxing the system by lowering its mechanical energy yields global morphogenetic predictions that accommodate the tendency towards this local preferred curvature, without explicitly modeling force-generating mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a three-dimensional spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions, on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.
Methods in Molecular Biology, 2012
Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for ... more Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for the life sciences. LSFM provides an exceptionally high imaging speed, high signal-to-noise ratio, low level of photo-bleaching, and good optical penetration depth. This unique combination of capabilities makes light sheet-based microscopes highly suitable for live imaging applications. Here, we provide an overview of light sheet-based microscopy assays for in vitro and in vivo imaging of biological samples, including cell extracts, soft gels, and large multicellular organisms. We furthermore describe computational tools for basic image processing and data inspection.