Alfredo Rodriguez

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function.

REPEATED STRESS ALTERS DENDRITIC SPINE MORPHOLOGY IN THE RAT MEDIAL PREFRONTAL CORTEX

Anatomical alterations in the medial prefrontal cortex (mPFC) are associated with hypothalamopituitary adrenal (HPA) axis dysregulation, altered stress hormone levels, and psychiatric symptoms of stress‐related mental illnesses. Functional imaging studies reveal impairment and shrinkage of the mPFC in such conditions, and these findings are paralleled by experimental studies showing dendritic retraction and spine loss following repeated stress in rodents. Here we extend this characterization to how repeated stress affects dendritic spine morphology in mPFC through the utilization of an automated approach that rapidly digitizes, reconstructs three dimensionally, and calculates geometric features of neurons. Rats were perfused after being subjected to 3 weeks of daily restraint stress (6 hours/day), and intracellular injections of Lucifer Yellow were made in layer II/III pyramidal neurons in the dorsal mPFC. To reveal spines in all angles of orientation, deconvolved high‐resolution confocal laser scanning microscopy image stacks of dendritic segments were reconstructed and analyzed for spine volume, surface area, and length using a Rayburst‐based automated approach (8,091 and 8,987 spines for control and stress, respectively). We found that repeated stress results in an overall decrease in mean dendritic spine volume and surface area, which was most pronounced in the distal portion of apical dendritic fields. Moreover, we observed an overall shift in the population of spines, manifested by a reduction in large spines and an increase in small spines. These results suggest a failure of spines to mature and stabilize following repeated stress and are likely to have major repercussions on function, receptor expression, and synaptic efficacy. J. Comp. Neurol. 507:1141–1150, 2008.

Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images

Precise quantification of complex three-dimensional (3D) structures from laser scanning microscopy (LSM) images is increasingly necessary in understanding normal function and pathologic processes in biology. This protocol describes a versatile shape analysis algorithm, Rayburst sampling, that generates automated 3D measurements from LSM images. Rayburst defines and efficiently casts a multidirectional core of rays from an interior point to the surface of a solid, allowing precise quantification of anisotropic and irregularly shaped 3D structures. Quantization error owing to the finite voxel representation in digital images is minimized by interpolating intensity values continuously between voxels. The Rayburst algorithm provides a primitive for the development of higher level algorithms that solve specific shape analysis problems. Examples are provided of applications to 3D neuronal morphometry: (i) estimation of diameters in tubular neuronal dendritic branching structures, and (ii) measurement of volumes and surface areas for dendritic spines and spatially complex histopathologic structures.

High-throughput, detailed, cell-specific neuroanatomy of dendritic spines using microinjection and confocal microscopy

Morphological features such as size, shape and density of dendritic spines have been shown to reflect important synaptic functional attributes and potential for plasticity. Here we describe in detail a protocol for obtaining detailed morphometric analysis of spines using microinjection of fluorescent dyes, high-resolution confocal microscopy, deconvolution and image analysis with NeuronStudio. Recent technical advancements include better preservation of tissue, resulting in prolonged ability to microinject, and algorithmic improvements that compensate for the residual z-smear inherent in all optical imaging. Confocal imaging parameters were probed systematically to identify both optimal resolution and the highest efficiency. When combined, our methods yield size and density measurements comparable to serial section transmission electron microscopy in a fraction of the time. An experiment containing three experimental groups with eight subjects each can take as little as 1 month if optimized for speed, or approximately 4–5 months if the highest resolution and morphometric detail is sought.

Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images.

Experimental and theoretical studies demonstrate that both global dendritic branching topology and fine spine geometry are crucial determinants of neuronal function, its plasticity and pathology. Importantly, simulation studies indicate that the interaction between local and global morphologic properties is pivotal in determining dendritic information processing and the induction of synapse-specific plasticity. The ability to reconstruct and quantify dendritic processes at high resolution is therefore an essential prerequisite to understanding the structural determinants of neuronal function. Existing methods of digitizing 3D neuronal structure use interactive manual computer tracing from 2D microscopy images. This method is time-consuming, subjective and lacks precision. In particular, fine details of dendritic varicosities, continuous dendritic taper, and spine morphology cannot be captured by these systems. We describe a technique for automated reconstruction of 3D neuronal morphology from multiple stacks of tiled confocal and multiphoton laser scanning microscopy (CLSM and MPLSM) images. The system is capable of representing both global and local structural variations, including gross dendritic branching topology, dendritic varicosities, and fine spine morphology with sufficient resolution for accurate 3D morphometric analyses and realistic biophysical compartment modeling. Our system provides a much needed tool for automated digitization and reconstruction of 3D neuronal morphology that reliably captures detail on spatial scales spanning several orders of magnitude, that avoids the subjective errors that arise during manual tracing with existing digitization systems, and that runs on a standard desktop workstation.

Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models

In neurodegenerative disorders, such as Alzheimer’s disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patch-clamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia.

Mutation Master: profiles of substitutions in hepatitis C virus RNA of the core, alternate reading frame, and NS2 coding regions.

The RNA genome of the hepatitis C virus (HCV) undergoes rapid evolutionary change. Efforts to control this virus would benefit from the advent of facile methods to identify characteristic features of HCV RNA and proteins, and to condense the vast amount of mutational data into a readily interpretable form. Many HCV sequences are available in GenBank. To facilitate analysis, consensus sequences were constructed to eliminate the overrepresentation of certain genotypes, such as genotype 1, and a novel package of sequence analysis tools was developed. Mutation Master generates profiles of point mutations in a population of sequences and produces a set of visual displays and tables indicating the number, frequency, and character of substitutions. It can be used to analyze hundreds of sequences at a time. When applied to 255 HCV core protein sequences, Mutation Master identified variable domains and a series of mutations meriting further investigation. It flagged position 4, for example, where 90% or more of all sequences in genotypes 1, 2, 4, and 5, have N4, whereas those in genotypes 3, 6, 7, 8, 9, and 10 have L4. This pattern is noteworthy: L (hydrophobic) to N (polar) substitutions are generally rare, and genotypes 1, 2, 4, and 5 do not form a recognized super family of sequences. Thus, the L4N substitution probably arose independently several times. Moreover, not one member of genotypes 1, 2, 4, or 5 has L4 and not one member of genotypes 3, 6, 7, 8, 9, or 10 has N4. This nonoverlapping pattern suggests that coordinated changes at position 4 and a second site are required to yield a viable virus. The package generated a table of genotype-specific substitutions whose future analysis may help to identify interacting amino acids. Three substitutions were present in 100% of genotype 2 members and absent from all others: A68D, R74K, and R114H. Finally, this study revealed thatARFP, a novel protein encoded in an overlapping reading frame, is as conserved as conventional HCV proteins, a result supporting a role for ARFP in the viral life cycle. Whereas most conventional programs for phylogenetic analysis of sequences provide information about overall relatedness of genes or genomes, this program highlights and profiles point mutations. This is important because determinants of pathogenicity and drug susceptibility are likely to result from changes at only one or two key nucleotides or amino acid sites, and would not be detected by the type of pairwise comparisons that have usually been performed on HCV to date. This study is the first application of Mutation Master, which is now available upon request (http://tandem.biomath.mssm.edu/mutationmaster.html).

Morphologic evidence for spatially clustered spines in apical dendrites of monkey neocortical pyramidal cells

The general organization of neocortical connectivity in rhesus monkey is relatively well understood. However, mounting evidence points to an organizing principle that involves clustered synapses at the level of individual dendrites. Several synaptic plasticity studies have reported cooperative interaction between neighboring synapses on a given dendritic branch, which may potentially induce synapse clusters. Additionally, theoretical models have predicted that such cooperativity is advantageous, in that it greatly enhances a neurons computational repertoire. However, largely because of the lack of sufficient morphologic data, the existence of clustered synapses in neurons on a global scale has never been established. The majority of excitatory synapses are found within dendritic spines. In this study, we demonstrate that spine clusters do exist on pyramidal neurons by analyzing the three‐dimensional locations of ∼40,000 spines on 280 apical dendritic branches in layer III of the rhesus monkey prefrontal cortex. By using clustering algorithms and Monte Carlo simulations, we quantify the probability that the observed extent of clustering does not occur randomly. This provides a measure that tests for spine clustering on a global scale, whenever high‐resolution morphologic data are available. Here we demonstrate that spine clusters occur significantly more frequently than expected by pure chance and that spine clustering is concentrated in apical terminal branches. These findings indicate that spine clustering is driven by systematic biological processes. We also found that mushroom‐shaped and stubby spines are predominant in clusters on dendritic segments that display prolific clustering, independently supporting a causal link between spine morphology and synaptic clustering. J. Comp. Neurol. 520:2888–2902, 2012.

Automatic Dendritic Spine Quantification from Confocal Data with Neurolucida 360.

Determining the density and morphology of dendritic spines is of high biological significance given the role of spines in synaptic plasticity and in neurodegenerative and neuropsychiatric disorders. Precise quantification of spines in three dimensions (3D) is essential for understanding the structural determinants of normal and pathological neuronal function. However, this quantification has been restricted to time‐ and labor‐intensive methods such as electron microscopy and manual counting, which have limited throughput and are impractical for studies of large samples. While there have been some automated software packages that quantify spine number, they are limited in terms of their characterization of spine structure. This unit presents methods for objective dendritic spine morphometric analysis by providing image acquisition parameters needed to ensure optimal data series for proper spine detection, characterization, and quantification with Neurolucida 360. These protocols will be a valuable reference for scientists working towards quantifying and characterizing spines.

Cortical Column and Whole Brain Imaging of Neural Circuits with Molecular Contrast and Nanoscale Resolution

Optical and electron microscopy have made tremendous inroads in understanding the complexity of the brain, but the former offers insufficient resolution to reveal subcellular details and the latter lacks the throughput and molecular contrast to visualize specific molecular constituents over mm-scale or larger dimensions. We combined expansion microscopy and lattice light sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire Drosophila brain, including synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly neuropil domain. The technology should enable statistically rich, large scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast. One Sentence Summary Combined expansion and lattice light sheet microscopy enables high speed, nanoscale molecular imaging of neural circuits over large volumes.