Limits...
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 process for automated quantification of fluorescent structures (Fish et al., 2008). (A–C) Show the intensity histograms with the lower bounds for segmentation progressively migrating towards higher values, which results in fewer and fewer objects being masked (A′–C′). At each step, only mask objects within the selected size range are kept (A′′–C′′). After each iterative step the resulting masks (A′′–C′′) are combined. Even after only these 3 iterative steps, the combined mask shown in (D′′) already has excellent object representation [compare with the unmasked data in (D’)]. In practice any number of iterations can be chosen so as to ensure comprehensive masking of objects (see e.g., Figures 2–5).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2903233&req=5

Figure 2: Iterative segmentation process for automated quantification of fluorescent structures (Fish et al., 2008). (A–C) Show the intensity histograms with the lower bounds for segmentation progressively migrating towards higher values, which results in fewer and fewer objects being masked (A′–C′). At each step, only mask objects within the selected size range are kept (A′′–C′′). After each iterative step the resulting masks (A′′–C′′) are combined. Even after only these 3 iterative steps, the combined mask shown in (D′′) already has excellent object representation [compare with the unmasked data in (D’)]. In practice any number of iterations can be chosen so as to ensure comprehensive masking of objects (see e.g., Figures 2–5).

Mentions: While manual counts of labeled structures of interest can be performed on a 3D image without further processing, the small size and large number of synaptic structures contained within a typical data set make it desirable to utilize automated approaches to identify structures of interest for quantification. Typically this is accomplished via threshold-based segmentation. In this process, a threshold value (either determined visually or by a thresholding algorithm) is applied to the fluorescence intensity histogram of the data set with the result that all pixels (or voxels) are reassigned a binary value according to whether they are below or above the threshold (which can be represented visually for example by assigning each value a separate color, e.g., black or blue, see Figure 2A). The resulting image is referred to as the image mask. A group of adjacent pixels (or voxels) that are above threshold and are surrounded by pixels (or voxels) that are below threshold create an object mask. For a 3D data set, the number of such object masks provides the count of the underlying objects within the image stack. For each object mask, data such as mean and total fluorescence intensity in each wavelength detected, area (or volume), and centroid (or center of volume) are readily generated.


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

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

Iterative segmentation process for automated quantification of fluorescent structures (Fish et al., 2008). (A–C) Show the intensity histograms with the lower bounds for segmentation progressively migrating towards higher values, which results in fewer and fewer objects being masked (A′–C′). At each step, only mask objects within the selected size range are kept (A′′–C′′). After each iterative step the resulting masks (A′′–C′′) are combined. Even after only these 3 iterative steps, the combined mask shown in (D′′) already has excellent object representation [compare with the unmasked data in (D’)]. In practice any number of iterations can be chosen so as to ensure comprehensive masking of objects (see e.g., Figures 2–5).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Iterative segmentation process for automated quantification of fluorescent structures (Fish et al., 2008). (A–C) Show the intensity histograms with the lower bounds for segmentation progressively migrating towards higher values, which results in fewer and fewer objects being masked (A′–C′). At each step, only mask objects within the selected size range are kept (A′′–C′′). After each iterative step the resulting masks (A′′–C′′) are combined. Even after only these 3 iterative steps, the combined mask shown in (D′′) already has excellent object representation [compare with the unmasked data in (D’)]. In practice any number of iterations can be chosen so as to ensure comprehensive masking of objects (see e.g., Figures 2–5).
Mentions: While manual counts of labeled structures of interest can be performed on a 3D image without further processing, the small size and large number of synaptic structures contained within a typical data set make it desirable to utilize automated approaches to identify structures of interest for quantification. Typically this is accomplished via threshold-based segmentation. In this process, a threshold value (either determined visually or by a thresholding algorithm) is applied to the fluorescence intensity histogram of the data set with the result that all pixels (or voxels) are reassigned a binary value according to whether they are below or above the threshold (which can be represented visually for example by assigning each value a separate color, e.g., black or blue, see Figure 2A). The resulting image is referred to as the image mask. A group of adjacent pixels (or voxels) that are above threshold and are surrounded by pixels (or voxels) that are below threshold create an object mask. For a 3D data set, the number of such object masks provides the count of the underlying objects within the image stack. For each object mask, data such as mean and total fluorescence intensity in each wavelength detected, area (or volume), and centroid (or center of volume) are readily generated.

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