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Mapping Synaptic Pathology within Cerebral Cortical Circuits in Subjects with Schizophrenia.

Sweet RA, Fish KN, Lewis DA - Front Hum Neurosci (2010)

Bottom Line: Efforts to localize these alterations in brain tissue from subjects with schizophrenia have frequently been limited to the quantification of structures that are non-selectively identified (e.g., dendritic spines labeled in Golgi preparations, axon boutons labeled with synaptophysin), or to quantification of proteins using methods unable to resolve relevant cellular compartments.An important adaptation required for studies of human disease is coupling this approach to stereologic methods for systematic random sampling of relevant brain regions.In this context, we provide examples of the examination of pre- and post-synaptic structures within excitatory and inhibitory circuits of the cerebral cortex.

View Article: PubMed Central - PubMed

Affiliation: Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.

ABSTRACT
Converging lines of evidence indicate that schizophrenia is characterized by impairments of synaptic machinery within cerebral cortical circuits. Efforts to localize these alterations in brain tissue from subjects with schizophrenia have frequently been limited to the quantification of structures that are non-selectively identified (e.g., dendritic spines labeled in Golgi preparations, axon boutons labeled with synaptophysin), or to quantification of proteins using methods unable to resolve relevant cellular compartments. Multiple label fluorescence confocal microscopy represents a means to circumvent many of these limitations, by concurrently extracting information regarding the number, morphology, and relative protein content of synaptic structures. An important adaptation required for studies of human disease is coupling this approach to stereologic methods for systematic random sampling of relevant brain regions. In this review article we consider the application of multiple label fluorescence confocal microscopy to the mapping of synaptic alterations in subjects with schizophrenia and describe the application of a novel, readily automated, iterative intensity/morphological segmentation algorithm for the extraction of information regarding synaptic structure number, size, and relative protein level from tissue sections obtained using unbiased stereological principles of sampling. In this context, we provide examples of the examination of pre- and post-synaptic structures within excitatory and inhibitory circuits of the cerebral cortex.

No MeSH data available.


Related in: MedlinePlus

Iterative segmentation effectively masks objects of varying fluorescent intensity (Fish et al., 2008). (A1) An image projection of the deconvolved data set, consisting of 11 z-planes. (A2) The same image is displayed using different intensity levels so that all the beads in the image stack can be easily discerned. (A3) The set of mask objects derived from the same data set using our iterative segmentation approach, note the low variability in size of the mask objects, reflecting the uniform size of the imaged beads. (B1) An image projection of a monkey DLPFC section labeled for synaptophysin after deconvolution. This data set underwent iterative segmentation with objects selected by size using a range of 0.0125–0.1 μm3 (B2) or a range of 0.0125–0.5 μm3 (B3). For each size range the final mask contained a relatively uniform set of object masks that approached the upper size limit of the selection criteria. While the smaller size selection provides greater resolution of individual labeled boutons, mask objects made with the larger size range cover the underlying bouton with greater fidelity.
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Figure 3: Iterative segmentation effectively masks objects of varying fluorescent intensity (Fish et al., 2008). (A1) An image projection of the deconvolved data set, consisting of 11 z-planes. (A2) The same image is displayed using different intensity levels so that all the beads in the image stack can be easily discerned. (A3) The set of mask objects derived from the same data set using our iterative segmentation approach, note the low variability in size of the mask objects, reflecting the uniform size of the imaged beads. (B1) An image projection of a monkey DLPFC section labeled for synaptophysin after deconvolution. This data set underwent iterative segmentation with objects selected by size using a range of 0.0125–0.1 μm3 (B2) or a range of 0.0125–0.5 μm3 (B3). For each size range the final mask contained a relatively uniform set of object masks that approached the upper size limit of the selection criteria. While the smaller size selection provides greater resolution of individual labeled boutons, mask objects made with the larger size range cover the underlying bouton with greater fidelity.

Mentions: We recently described a novel approach to masking objects in 3D data sets which resulted in improved object masking in thick brain tissue sections (Fish et al., 2008). Rather than choosing a single set of intensity thresholds to create a binary selection mask, our approach used multiple iterations that systematically varied the intensity thresholds and combined the resultant masks via morphologic selection criteria (Figures 2A–D). The use of varying intensity thresholds is particularly adapted for the selection of objects without uniform and/or high fluorescence intensities, which results when proteins differ in their abundance within synaptic structures, and can vary substantially over the course of development and as a result of disease states. The advantage of our approach over the use of a single threshold can be readily seen imaging a set of beads of uniform size, but of varying fluorescence intensity (Figures 3A1–A3). The iterative method works similarly well for masking axon boutons labeled with an antibody to synaptophysin in primate cortex. Both bouton size and synaptophysin expression vary substantially in normal cortex, and yet are effectively masked to render accurate structure counts and represent with high fidelity structure shape (Figures 3B1–B3).


Mapping Synaptic Pathology within Cerebral Cortical Circuits in Subjects with Schizophrenia.

Sweet RA, Fish KN, Lewis DA - Front Hum Neurosci (2010)

Iterative segmentation effectively masks objects of varying fluorescent intensity (Fish et al., 2008). (A1) An image projection of the deconvolved data set, consisting of 11 z-planes. (A2) The same image is displayed using different intensity levels so that all the beads in the image stack can be easily discerned. (A3) The set of mask objects derived from the same data set using our iterative segmentation approach, note the low variability in size of the mask objects, reflecting the uniform size of the imaged beads. (B1) An image projection of a monkey DLPFC section labeled for synaptophysin after deconvolution. This data set underwent iterative segmentation with objects selected by size using a range of 0.0125–0.1 μm3 (B2) or a range of 0.0125–0.5 μm3 (B3). For each size range the final mask contained a relatively uniform set of object masks that approached the upper size limit of the selection criteria. While the smaller size selection provides greater resolution of individual labeled boutons, mask objects made with the larger size range cover the underlying bouton with greater fidelity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2903233&req=5

Figure 3: Iterative segmentation effectively masks objects of varying fluorescent intensity (Fish et al., 2008). (A1) An image projection of the deconvolved data set, consisting of 11 z-planes. (A2) The same image is displayed using different intensity levels so that all the beads in the image stack can be easily discerned. (A3) The set of mask objects derived from the same data set using our iterative segmentation approach, note the low variability in size of the mask objects, reflecting the uniform size of the imaged beads. (B1) An image projection of a monkey DLPFC section labeled for synaptophysin after deconvolution. This data set underwent iterative segmentation with objects selected by size using a range of 0.0125–0.1 μm3 (B2) or a range of 0.0125–0.5 μm3 (B3). For each size range the final mask contained a relatively uniform set of object masks that approached the upper size limit of the selection criteria. While the smaller size selection provides greater resolution of individual labeled boutons, mask objects made with the larger size range cover the underlying bouton with greater fidelity.
Mentions: We recently described a novel approach to masking objects in 3D data sets which resulted in improved object masking in thick brain tissue sections (Fish et al., 2008). Rather than choosing a single set of intensity thresholds to create a binary selection mask, our approach used multiple iterations that systematically varied the intensity thresholds and combined the resultant masks via morphologic selection criteria (Figures 2A–D). The use of varying intensity thresholds is particularly adapted for the selection of objects without uniform and/or high fluorescence intensities, which results when proteins differ in their abundance within synaptic structures, and can vary substantially over the course of development and as a result of disease states. The advantage of our approach over the use of a single threshold can be readily seen imaging a set of beads of uniform size, but of varying fluorescence intensity (Figures 3A1–A3). The iterative method works similarly well for masking axon boutons labeled with an antibody to synaptophysin in primate cortex. Both bouton size and synaptophysin expression vary substantially in normal cortex, and yet are effectively masked to render accurate structure counts and represent with high fidelity structure shape (Figures 3B1–B3).

Bottom Line: Efforts to localize these alterations in brain tissue from subjects with schizophrenia have frequently been limited to the quantification of structures that are non-selectively identified (e.g., dendritic spines labeled in Golgi preparations, axon boutons labeled with synaptophysin), or to quantification of proteins using methods unable to resolve relevant cellular compartments.An important adaptation required for studies of human disease is coupling this approach to stereologic methods for systematic random sampling of relevant brain regions.In this context, we provide examples of the examination of pre- and post-synaptic structures within excitatory and inhibitory circuits of the cerebral cortex.

View Article: PubMed Central - PubMed

Affiliation: Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.

ABSTRACT
Converging lines of evidence indicate that schizophrenia is characterized by impairments of synaptic machinery within cerebral cortical circuits. Efforts to localize these alterations in brain tissue from subjects with schizophrenia have frequently been limited to the quantification of structures that are non-selectively identified (e.g., dendritic spines labeled in Golgi preparations, axon boutons labeled with synaptophysin), or to quantification of proteins using methods unable to resolve relevant cellular compartments. Multiple label fluorescence confocal microscopy represents a means to circumvent many of these limitations, by concurrently extracting information regarding the number, morphology, and relative protein content of synaptic structures. An important adaptation required for studies of human disease is coupling this approach to stereologic methods for systematic random sampling of relevant brain regions. In this review article we consider the application of multiple label fluorescence confocal microscopy to the mapping of synaptic alterations in subjects with schizophrenia and describe the application of a novel, readily automated, iterative intensity/morphological segmentation algorithm for the extraction of information regarding synaptic structure number, size, and relative protein level from tissue sections obtained using unbiased stereological principles of sampling. In this context, we provide examples of the examination of pre- and post-synaptic structures within excitatory and inhibitory circuits of the cerebral cortex.

No MeSH data available.


Related in: MedlinePlus