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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

Example of a typical stereologic sampling scheme incorporating confocal microscopy with post-processing object masking. (A). A schematic of a human brain, showing the superior temporal gyrus (STG) which can be sampled systematic uniformly random, shown here as a series of numbered blocks from which every other block (yellow) is selected for further processing. (B) Each of the selected blocks is further subsampled into a systematic random series of sections for microscopy [shown here for one of the blocks, with every other section in this example selected for further processing (yellow)]. (C) Within each sampled section the region of interest is identified (e.g., shown here as the yellow outline of deep layer 3 within the larger cytoarchitectonically defined Primary Auditory Cortex area of interest shown in dark gray). (D) An enlarged view of a portion of the region of interest showing a systematic random sampling grid which has been placed over each sampled section. Sites for microscopic sampling are identified by each grid intersection with the region of interest. (E) An enlarged view of a single sampling site, showing the optical disector located within the z-axis of the tissue section. The gray outlines represent the projections of the disector unto the superficial and deep surfaces of the tissue section. (F) A series of 2D confocal images at a fixed distance apart in the z-axis (see Other Technical Issues for a discussion of appropriate z-axis sampling distances) are collected at each sampling site. Immunoreactive synaptic structures are shown as red puncta. (G) The series of images are combined to form a 3D data set from each site, resulting in a sampled cube with x and y dimensions represented here in pixels and the z dimension as plane number. Within this data set, the disector can be conceived of as defined by a set of values in 3D. (H) Using the masking approach described above, a center of volume, that is a single point defined by x, y, z coordinates, can be assigned for each immunoreactive puncta. Puncta with centers of volume falling within the boundaries of the optical disector are sampled for quantification (shown here in green), while those puncta with centers of volume falling outside the disector, or touching one of the exclusion lines (red) are not selected for quantification. SF, Sylvian fissure; STS, superior temporal sulcus.
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Figure 4: Example of a typical stereologic sampling scheme incorporating confocal microscopy with post-processing object masking. (A). A schematic of a human brain, showing the superior temporal gyrus (STG) which can be sampled systematic uniformly random, shown here as a series of numbered blocks from which every other block (yellow) is selected for further processing. (B) Each of the selected blocks is further subsampled into a systematic random series of sections for microscopy [shown here for one of the blocks, with every other section in this example selected for further processing (yellow)]. (C) Within each sampled section the region of interest is identified (e.g., shown here as the yellow outline of deep layer 3 within the larger cytoarchitectonically defined Primary Auditory Cortex area of interest shown in dark gray). (D) An enlarged view of a portion of the region of interest showing a systematic random sampling grid which has been placed over each sampled section. Sites for microscopic sampling are identified by each grid intersection with the region of interest. (E) An enlarged view of a single sampling site, showing the optical disector located within the z-axis of the tissue section. The gray outlines represent the projections of the disector unto the superficial and deep surfaces of the tissue section. (F) A series of 2D confocal images at a fixed distance apart in the z-axis (see Other Technical Issues for a discussion of appropriate z-axis sampling distances) are collected at each sampling site. Immunoreactive synaptic structures are shown as red puncta. (G) The series of images are combined to form a 3D data set from each site, resulting in a sampled cube with x and y dimensions represented here in pixels and the z dimension as plane number. Within this data set, the disector can be conceived of as defined by a set of values in 3D. (H) Using the masking approach described above, a center of volume, that is a single point defined by x, y, z coordinates, can be assigned for each immunoreactive puncta. Puncta with centers of volume falling within the boundaries of the optical disector are sampled for quantification (shown here in green), while those puncta with centers of volume falling outside the disector, or touching one of the exclusion lines (red) are not selected for quantification. SF, Sylvian fissure; STS, superior temporal sulcus.

Mentions: The comparison of spinning disk and LSCM is summarized in Table 1. Knowledge of these tradeoffs can be used to select between microscopy approaches so as to enhance study design in imaging synaptic structures. For example, when maximal spatial resolution is required (e.g., see Figure 9) using a LSCM may prove most appropriate. When high throughput of multi-wavelength image stacks is needed (e.g., for stereologic sampling of a large cohort of subjects), spinning disk confocal microscopy is highly beneficial (Figure 4). Finally, because spinning disk confocal microscopes fall in between epifluorescence systems and LSCM with regard to both fluorescence quantification and the ability to discriminate densely packed small structures (especially when combined with deconvolution algorithms, see Deconvolution and Image Segmentation), they are often the ideal platform for studies in which both discrimination of structures for quantification of numbers and extraction of information on relative antigen expression are desired.


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

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

Example of a typical stereologic sampling scheme incorporating confocal microscopy with post-processing object masking. (A). A schematic of a human brain, showing the superior temporal gyrus (STG) which can be sampled systematic uniformly random, shown here as a series of numbered blocks from which every other block (yellow) is selected for further processing. (B) Each of the selected blocks is further subsampled into a systematic random series of sections for microscopy [shown here for one of the blocks, with every other section in this example selected for further processing (yellow)]. (C) Within each sampled section the region of interest is identified (e.g., shown here as the yellow outline of deep layer 3 within the larger cytoarchitectonically defined Primary Auditory Cortex area of interest shown in dark gray). (D) An enlarged view of a portion of the region of interest showing a systematic random sampling grid which has been placed over each sampled section. Sites for microscopic sampling are identified by each grid intersection with the region of interest. (E) An enlarged view of a single sampling site, showing the optical disector located within the z-axis of the tissue section. The gray outlines represent the projections of the disector unto the superficial and deep surfaces of the tissue section. (F) A series of 2D confocal images at a fixed distance apart in the z-axis (see Other Technical Issues for a discussion of appropriate z-axis sampling distances) are collected at each sampling site. Immunoreactive synaptic structures are shown as red puncta. (G) The series of images are combined to form a 3D data set from each site, resulting in a sampled cube with x and y dimensions represented here in pixels and the z dimension as plane number. Within this data set, the disector can be conceived of as defined by a set of values in 3D. (H) Using the masking approach described above, a center of volume, that is a single point defined by x, y, z coordinates, can be assigned for each immunoreactive puncta. Puncta with centers of volume falling within the boundaries of the optical disector are sampled for quantification (shown here in green), while those puncta with centers of volume falling outside the disector, or touching one of the exclusion lines (red) are not selected for quantification. SF, Sylvian fissure; STS, superior temporal sulcus.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Example of a typical stereologic sampling scheme incorporating confocal microscopy with post-processing object masking. (A). A schematic of a human brain, showing the superior temporal gyrus (STG) which can be sampled systematic uniformly random, shown here as a series of numbered blocks from which every other block (yellow) is selected for further processing. (B) Each of the selected blocks is further subsampled into a systematic random series of sections for microscopy [shown here for one of the blocks, with every other section in this example selected for further processing (yellow)]. (C) Within each sampled section the region of interest is identified (e.g., shown here as the yellow outline of deep layer 3 within the larger cytoarchitectonically defined Primary Auditory Cortex area of interest shown in dark gray). (D) An enlarged view of a portion of the region of interest showing a systematic random sampling grid which has been placed over each sampled section. Sites for microscopic sampling are identified by each grid intersection with the region of interest. (E) An enlarged view of a single sampling site, showing the optical disector located within the z-axis of the tissue section. The gray outlines represent the projections of the disector unto the superficial and deep surfaces of the tissue section. (F) A series of 2D confocal images at a fixed distance apart in the z-axis (see Other Technical Issues for a discussion of appropriate z-axis sampling distances) are collected at each sampling site. Immunoreactive synaptic structures are shown as red puncta. (G) The series of images are combined to form a 3D data set from each site, resulting in a sampled cube with x and y dimensions represented here in pixels and the z dimension as plane number. Within this data set, the disector can be conceived of as defined by a set of values in 3D. (H) Using the masking approach described above, a center of volume, that is a single point defined by x, y, z coordinates, can be assigned for each immunoreactive puncta. Puncta with centers of volume falling within the boundaries of the optical disector are sampled for quantification (shown here in green), while those puncta with centers of volume falling outside the disector, or touching one of the exclusion lines (red) are not selected for quantification. SF, Sylvian fissure; STS, superior temporal sulcus.
Mentions: The comparison of spinning disk and LSCM is summarized in Table 1. Knowledge of these tradeoffs can be used to select between microscopy approaches so as to enhance study design in imaging synaptic structures. For example, when maximal spatial resolution is required (e.g., see Figure 9) using a LSCM may prove most appropriate. When high throughput of multi-wavelength image stacks is needed (e.g., for stereologic sampling of a large cohort of subjects), spinning disk confocal microscopy is highly beneficial (Figure 4). Finally, because spinning disk confocal microscopes fall in between epifluorescence systems and LSCM with regard to both fluorescence quantification and the ability to discriminate densely packed small structures (especially when combined with deconvolution algorithms, see Deconvolution and Image Segmentation), they are often the ideal platform for studies in which both discrimination of structures for quantification of numbers and extraction of information on relative antigen expression are desired.

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