“Acquiring Practical Population Estimates of Neurons Through Design-Based Stereology: Dissecting the Disector.” Zadory D, Burton E, Wolf JC. The 44th Annual Meeting of the Society for Neuroscience. Washington, DC. November 19, 2014.
For full-resolution viewing, please open or save as a PDF.
Neuron Analysis Workshop: Neuron Tracing from Tissue Specimens at the MicroscopeMBF Bioscience
This presentation is from a workshop about neuron reconstruction. It reviews the process of neuron tracing directly from tissue specimens at the microscopy to study neuron morphology.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Neuron Analysis Workshop: Neuron Tracing from Tissue Specimens at the MicroscopeMBF Bioscience
This presentation is from a workshop about neuron reconstruction. It reviews the process of neuron tracing directly from tissue specimens at the microscopy to study neuron morphology.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Image Quality, Artifacts and it's Remedies in CT-Avinesh ShresthaAvinesh Shrestha
CT is one of the frequently used diagnostic imaging modalities in Radiology. Knowledge about image quality and artifacts is essential when diagnosing a patient with the help of CT images. Moreover, Radiology Technologist's should be very well aware about the ways to identify and eliminate or minimize the artifacts in CT for better image quality.
Image registraion is vital component in modern radiotherpay. Accuracy is important as output of image registraion process is input of another process in radiation therapy
Spatial analysis of images sensed and captured from a satellite provides less adequate information about a
remote location. Hence spectral analysis becomes essential. Hyperspectral image is one of the remotely
sensed images, superior to multispectral images in providing spectral information. Target detection is one
of the significant requirements in many areas such as military, agriculture etc. Sub pixel target detection,
which further divides each pixel of the image into partitions, is possible only with spectral analysis of
hyperspectral image. This paper focuses on developing an algorithm for segmenting hyperspectral image
using sub pixel target detection followed by Fuzzy C-Means(FCM) clustering technique. Principal
Component Analysis (PCA) is the basic step adopted to reduce the high dimensional data of image to low
dimensional data. Mixture tuned matched filtering technique is used for sub pixel target detection because
it is a combination of linear spectral unmixing and matched filtering and has advantages of both the
techniques.
Image registration and data fusion techniques.pptx latest saveM'dee Phechudi
Medical imaging is the fundamental tool in conformal radiation therapy. Almost every aspect of patient management involves some form of two or three dimensional image data acquired using one or more modality.
Image data are now used for diagnosis and staging, for treatment planning and delivery and for monitoring patients after therapy.
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
Lec13: Clustering Based Medical Image Segmentation MethodsUlaş Bağcı
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
i am HAFIZ M WASEEM from mailsi vehari
BSc in science college Multan Pakistan
MSC university of education Lahore Pakistan
I love Pakistan and my teachers
Michael DeBrota et al. - Assessment of Computational Histopathology in Thorac...Michael DeBrota
Background and Hypothesis:
Thoracic aortic aneurysm (TAA) histopathology includes elastic fiber (EF) abnormalities, mucoid extracellular matrix (MECM) accumulation, and smooth muscle derangement in the aortic medial layer. While semi-quantitative grading of these characteristics is a standard practice, computational characterization of medial layer components may facilitate novel quantitative analyses at higher throughput. We hypothesized that computational results would correlate with results of semi-quantitative grading of aortic histopathology.
Experimental Design:
Formalin-fixed, paraffin-embedded human aortic tissue sections were stained with Movat’s pentachrome to characterize aortic microstructure. Sections were also immunostained for nitrotyrosine residues to assess oxidative stress. Samples were initially graded semi-quantitatively by two independent blinded readers. Next, computational histopathology software was used a) to quantify the proportions of EF, MECM, and cellular area in the medial layer of pentachrome-stained sections and b) to quantify the distribution and intensity of positive nitrotyrosine staining in immunostained sections. Association between semi-quantitative grading and computed values was tested with ANOVA.
Results:
The cohort included 74 participants who underwent prophylactic aortic replacement for TAA and 23 healthy controls. The mean age was 54±17 years. On average, EFs accounted for 49% (range 6-90%) of medial tissue area, whereas MECM accounted for 25% (1-73%). The overall semi-quantitative grade of medial degeneration severity was associated with decrease in EF fraction (p=0.02). The grade of EF thinning also strongly correlated with decrease in EF fraction (p=1x10-6). Meanwhile, grade for accumulation of MECM was associated with increase in MECM (p=0.004). Increased semi-quantitative grading for nitrotyrosine levels was associated with increased nuclear signal optical density (p=9x10-10) and greater percentage of cells labeled as strongly positive (p=8x10-10).
Conclusion and Potential Impact:
We observed significant correlations between computed quantitative values and semi-quantitative grading. This suggests that computational histopathology is a valid method for investigation of human TAA tissues.
Image Quality, Artifacts and it's Remedies in CT-Avinesh ShresthaAvinesh Shrestha
CT is one of the frequently used diagnostic imaging modalities in Radiology. Knowledge about image quality and artifacts is essential when diagnosing a patient with the help of CT images. Moreover, Radiology Technologist's should be very well aware about the ways to identify and eliminate or minimize the artifacts in CT for better image quality.
Image registraion is vital component in modern radiotherpay. Accuracy is important as output of image registraion process is input of another process in radiation therapy
Spatial analysis of images sensed and captured from a satellite provides less adequate information about a
remote location. Hence spectral analysis becomes essential. Hyperspectral image is one of the remotely
sensed images, superior to multispectral images in providing spectral information. Target detection is one
of the significant requirements in many areas such as military, agriculture etc. Sub pixel target detection,
which further divides each pixel of the image into partitions, is possible only with spectral analysis of
hyperspectral image. This paper focuses on developing an algorithm for segmenting hyperspectral image
using sub pixel target detection followed by Fuzzy C-Means(FCM) clustering technique. Principal
Component Analysis (PCA) is the basic step adopted to reduce the high dimensional data of image to low
dimensional data. Mixture tuned matched filtering technique is used for sub pixel target detection because
it is a combination of linear spectral unmixing and matched filtering and has advantages of both the
techniques.
Image registration and data fusion techniques.pptx latest saveM'dee Phechudi
Medical imaging is the fundamental tool in conformal radiation therapy. Almost every aspect of patient management involves some form of two or three dimensional image data acquired using one or more modality.
Image data are now used for diagnosis and staging, for treatment planning and delivery and for monitoring patients after therapy.
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
Lec13: Clustering Based Medical Image Segmentation MethodsUlaş Bağcı
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
i am HAFIZ M WASEEM from mailsi vehari
BSc in science college Multan Pakistan
MSC university of education Lahore Pakistan
I love Pakistan and my teachers
Michael DeBrota et al. - Assessment of Computational Histopathology in Thorac...Michael DeBrota
Background and Hypothesis:
Thoracic aortic aneurysm (TAA) histopathology includes elastic fiber (EF) abnormalities, mucoid extracellular matrix (MECM) accumulation, and smooth muscle derangement in the aortic medial layer. While semi-quantitative grading of these characteristics is a standard practice, computational characterization of medial layer components may facilitate novel quantitative analyses at higher throughput. We hypothesized that computational results would correlate with results of semi-quantitative grading of aortic histopathology.
Experimental Design:
Formalin-fixed, paraffin-embedded human aortic tissue sections were stained with Movat’s pentachrome to characterize aortic microstructure. Sections were also immunostained for nitrotyrosine residues to assess oxidative stress. Samples were initially graded semi-quantitatively by two independent blinded readers. Next, computational histopathology software was used a) to quantify the proportions of EF, MECM, and cellular area in the medial layer of pentachrome-stained sections and b) to quantify the distribution and intensity of positive nitrotyrosine staining in immunostained sections. Association between semi-quantitative grading and computed values was tested with ANOVA.
Results:
The cohort included 74 participants who underwent prophylactic aortic replacement for TAA and 23 healthy controls. The mean age was 54±17 years. On average, EFs accounted for 49% (range 6-90%) of medial tissue area, whereas MECM accounted for 25% (1-73%). The overall semi-quantitative grade of medial degeneration severity was associated with decrease in EF fraction (p=0.02). The grade of EF thinning also strongly correlated with decrease in EF fraction (p=1x10-6). Meanwhile, grade for accumulation of MECM was associated with increase in MECM (p=0.004). Increased semi-quantitative grading for nitrotyrosine levels was associated with increased nuclear signal optical density (p=9x10-10) and greater percentage of cells labeled as strongly positive (p=8x10-10).
Conclusion and Potential Impact:
We observed significant correlations between computed quantitative values and semi-quantitative grading. This suggests that computational histopathology is a valid method for investigation of human TAA tissues.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.
DevFest19 - Early Diagnosis of Chronic Diseases by Smartphone AIGaurav Kheterpal
Session by Sabyasachi Mukhopadhyay
Kolkata Lead, Facebook Developer Circle
GDE in ML
Intel Software Innovator
Visiting Faculty, SCIT Pune
Co-Founder & Chief Research Officer, Twelit MedTech Pvt. Ltd
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
In this paper we present the use of a signal processing technique to find dominant channels in
near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring
channels and identify delays among the channels. In addition, visual inspection was used to
detect potential dominant channels. The results showed that the visual analysis exposed painrelated
activations in the primary somatosensory cortex (S1) after stimulation which is
consistent with similar studies and the cross correlation analysis found dominant channels on
both cerebral hemispheres. The analysis also showed a relationship between dominant channels
and neighbouring channels. Therefore, our results present a new method to detect dominant
regions in the cerebral cortex using near-infrared spectroscopy. These results have also
implications in the reduction of number of channels by eliminating irrelevant channels for the
experiment.
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Dietary Administration of Diquat for 13 Weeks Does Not Result in a Loss of Do...EPL, Inc.
Dietary Administration of Diquat for 13 Weeks Does Not Result in a Loss of Dopaminergic Neurons in the Substantia Nigra Pars Compacta (SNpc) of C57BL/6J Mice
Renal Tubular Pigmentation Associated with Senna-Related MetabolitesEPL, Inc.
“Renal Tubular Pigmentation Associated with Senna-Related Metabolites.” Willson GA (presenter) Malarkey DE, Allison N, Harris N, Miller RA. The 54th Annual Society of Toxicology Meeting. San Diego, CA. March 25, 2015.
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The National Toxicology Program Nonneoplastic Lesion AtlasEPL, Inc.
The National Toxicology Program (NTP)’s Nonneoplastic Lesion Atlas is a valuable, web-based resource with thousands of high-quality, enlargeable images, diagnostic
guidelines, and preferred NTP terminology for numerous nonneoplastic rodent lesions. The atlas will be used by the NTP and its many pathology partners to standardize lesion diagnosis, terminology, and the way lesions are recorded in NTP studies. The goal is to improve the consistency and accuracy of the diagnosis of nonneoplastic lesions between pathologists and laboratories to improve the organization and utility of the NTP’s nonneoplastic lesion database and, ultimately, our understanding of nonneoplastic lesions. The NTP Nonneoplastic Lesion Atlas is a living document that complements the INHAND publications. In fact, one of the aims of the atlas is to align the NTP terminology with that of the INHAND publications as much as possible. The atlas is also a useful training tool for pathology residents and can be used by any organization to improve their own nonneoplastic lesion database. A total of 56 organs organized into 13 organ systems will be included in the completed project. The atlas is free to the public at http://ntp.niehs.nih.gov/nnl.
Subchronic Inhalation Exposure of Rats to Libby Amphibole and Amosite Asbesto...EPL, Inc.
“Subchronic Inhalation Exposure of Rats to Libby Amphibole and Amosite Asbestos: Effects at 18 Months Post Exposure.” Willson GA (presenter), Dodd DA, Roberts KC, Wall HG, Jarabek AM, Gavett SH. The 33rd Society of Toxicologic Pathology Annual Meeting. Washington, DC. June 22-26, 2014.
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For full-resolution viewing, please open or save as a PDF.
Immunohistochemical Characterization of ENU-induced Brain Tumors in F344 RatsEPL, Inc.
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For full-resolution viewing, please open or save file as a PDF.
EPL, Inc. - Comparative Anatomy of the EyeEPL, Inc.
Proper evaluation of potential effects of test articles on the eye and its components requires attention to detail in the preparation and examination of specimens. EPL has developed critical capability in histology to produce consistent microscopic sections that facilitate the pathologist’s evaluation of the various segments of the eye. The EPL methodology is applicable to all routes of administration, including intravitreal, topical, or systemic. The veterinary pathologists at EPL have many years of experience evaluating effects on the eye; EPL is the premier source for ocular pathology evaluation. In addition to standard histology and histopathology, special preparations may be included (e.g., special histochemical stains, immunohistochemistry) to further characterize lesions.
The main purpose of a Pathology Peer Review is to improve the quality of the pathology data and narrative. This can be achieved in several ways. A peer review can help to ensure that the data are presented in a manner that meets the requirements of the regulatory agency to which the data will be submitted. A peer review may help to increase the accuracy of the data, thus increasing the confidence in it both for the Sponsor and for the regulatory agencies. This is true whether the study was completed at the Sponsor’s facility, at a Contract Research Organization (CRO), or in an academic laboratory. A peer review can also confirm the target organs identified in the original evaluation and confirm the No Observed Effect Level (NOEL). Our unique PQA software program is a computerized pathology peer review procedure developed at EPL that operates on a portable laptop computer. Study data can be interfaced between the laboratory and EPL’s pathology peer review program through the use of electronic ASCII text files. This assures that the accuracy of the data is maintained during the entire review process and that comparison between the preliminary histopathology data from the laboratory and the pathology quality assurance audit can be made rapidly. In this manner, differences can be easily identified and resolved by the study pathologist and reviewing pathologist, thus allowing the laboratory to complete the draft study report in a timely manner.
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
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Toxic effects of heavy metals : Lead and Arsenicsanjana502982
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Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
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Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Acquiring Practical Population Estimates of Neurons Through Design-Based Stereology: Dissecting the Disector
1. Acquiring Practical Population Estimates of Neurons Through Design-Based Stereology: Dissecting the Disector
ABSTRACT
A design-based stereological probe known as the optical disector is employed frequently to acquire unbiased neuronal population estimates from thick histologic sections. This methodology includes customizable parameters for systematic sampling through the X, Y, and Z axes of the region of interest (ROI). For the purpose of this study, cryosections of 40 μm nominal thickness were immunostained for tyrosine hydroxylase (TH) to detect dopaminergic neurons in the substantia nigra pars compacta (SNpc) of C57BL/6J mice. Stereological estimates of TH+ neuron populations were acquired using the Optical Fractionator Workflow module of the Stereo Investigator software system (MBF Bioscience). Because the histologic processing and immunostaining of thick sections may result in non-uniform distribution and density of TH+ neurons throughout the Z axis of the SNpc, various disector heights and placements were analyzed systematically to determine the degree to which disector height selection contributed substantially to the total population estimates. We concluded that the choice of disector height had a major influence on total population estimates of TH+ neurons in the SNpc. For studies that utilize the optical disector method to quantify neuronal population estimates, adequate preliminary sampling should be performed initially through the entire Z axis with extrapolation of the data in order to achieve accurate population estimates.
Daniel Zadory, Ellen Burton, Jeffrey Wolf. Experimental Pathology Laboratories, Inc.
OBJECTIVE
Evaluate the optical disector component of the design-based stereological probe, the optical fractionator, through various disector heights and placements to determine their impact on stereologically-derived TH+ neuron estimates in the SNpc of C57BL/6J mice.
RESULTS
CONCLUSIONS
Optimally, stereological estimates obtained using the optical fractionator approach should be based on tissue sections in which the distribution of objects (e.g., neurons) is uniform throughout the Z-depth. However, obtaining thick sections with entirely uniform cell distributions can be technically challenging. The impact of disector height and placement on total population estimates in thick histologic sections can be established by sampling through the entire Z-depth. For the biphasic distribution pattern that we typically encounter, the most consistent and most accurate results were obtained when disectors were placed so as to encompass symmetrical portions of the z-depth distribution curve. A recently released Stereo Investigator module called Resample Disector allows for similar preliminary analysis of sampling parameters through the Z- depth.
RESULTS
REFERENCES
•West M.J., Slomianka L., Gundersen H.J., (1991); Unbiased stereological estimation of the total number of neurons in the subdivisions of the rat hippocampus using the optical fractionator. Anat Rec; 231:482.
•Baquet Z.C., Williams D., Brody J., Smeyne R.J., (2009); A comparison of model-based (2D) and design-based (3D) stereological methods for estimating cell number in the substantia nigra pars compacta (SNpc) of the C57BL/6J mouse. Neuroscience; 161(4):1082-90. doi: 10.1016/j.neuroscience.2009.04.031.
•Torres E.M., Meldrum A., Kirik D., Dunnett S., (2006); An investigation of the problem of two-layered Immunohistochemical staining in paraformaldehyde fixed sections. Journal of Neuroscience Methods; 158, 66-74
•Carlo C.N., Stevens C.F., (2011); Analysis of differential shrinkage in frozen brain sections and its implications for the use of guard zones in stereology. Journal of Comparative Neurology; 519:2803-2810
•Gardella D., Hatton W.J., Rind H.B., Rosen G.D., von Bartheld C.S., (2003); Differential tissue shrinkage and compression in the z-axis: implications for optical disector counting in vibratome-, plastic- and cryosections. Journal of Neuroscience Methods; 124, 45-59
INTRODUCTION
The purpose of design-based stereology is to efficiently obtain precise and unbiased morphometric estimates of specific features in whole organs or anatomical structures by subsampling thru three-dimensional (3D) axes of the target region. Earlier two-dimensional (2D) model-based stereological methods relied on parameters ascertained through qualitative assumptions of volume, size, density, orientation, and shape that introduced varying degrees of bias. While both design- based and model-based stereological methods have strengths and weaknesses, it is imperative that these limitations are understood by researchers, so that reproducible, accurate, and unbiased estimates can be generated, and results from different laboratories can be reasonably compared (Baquet et al., 2009).
A widely used design-based stereology probe, the optical fractionator, is employed frequently to quantify neurons in thick histologic sections as a measure of pharmacological efficacy or neurotoxicity. Through unbiased systematic uniform random sampling in a 3D space, this approach combines two elements of previously established stereological probes, the Optical Disector and Fractionator (West et al., 1991). Population estimates are derived through the following formula:
METHODS
•Specimen Collection and Processing
Twenty male C57BL/6J mice of approximately 11-12 weeks old were anesthetized by intraperitoneal injection of sodium pentobarbital and transcardially perfused with physiological saline followed by 4% paraformaldehyde (PFA). The intact brains were removed and post-fixed in 4% PFA overnight at 4⁰ C, followed by separate immersion fixations of 10% and 30% sucrose solution at 4⁰ C for 24 hours and 48 hours, respectively, to initiate cryoprotection. Following cryoprotection, the brains were flash frozen for approximately 35 seconds in isopentane chilled to -40⁰ C.
METHODS
•Immunohistochemical Detection of TH+ Neurons in the SNpc
Each brain was microtomed serially in the coronal plane (i.e., transversely along the rostral-caudal axis) at 40 μm intervals using a sliding microtome and systematically transferred into a 24-well plate containing 30% ethylene glycol solution and stored at - 20⁰ C. A section interval of 1:3 was identified and briefly washed in 0.1 M phosphate buffered saline (PBS) followed by a 10 minute incubation in 3% H2O2 in PBS to quench endogenous peroxidase activity. Non-specific binding of antibodies to targets was blocked by immersion of the sections in a solution of 10% normal goat serum and 1% bovine serum albumin, made up in PBS for one hour at room temperature. Immunostaining was performed according to the standard avidin-biotin complex (ABC) method. Sections were then incubated in a solution containing the primary Rabbit Polyclonal Anti-Tyrosine Hydroxylase (EMD Millipore, Billerica, MA) antibody at a dilution of 1:4000 for 48 hours at 4⁰C, and then in the secondary antibody, biotinylated goat anti-rabbit IgG (Vector Laboratories, Inc.), at a dilution of 1:6000 and incubated for another 2 hours at room temperature. The brain sections were then incubated for 60 minutes in an ABC solution (Standard VectaStain Elite Kit, Vector Laboratories, Inc.) at room temperature. Visualization of the reaction was accomplished by immersion in 0.03% 3,3-diaminobenzidine (DAB, Sigma Fast Tablets, Sigma-Aldrich) for 1 minute at room temperature. Sections were then floated onto positively charged glass slides and air-dried for approximately 1 hour at room temperature. Following a succession of rinses with ethanol (100%, 95%, and 70%, respectively) and distilled water, sections were then counterstained FD Cresyl Violet Solution and cover slipped using a resinous mounting medium.
•Design-Based Stereology
The total numbers of TH+ neurons in the SNpc were estimated using the optical fractionator approach (West et. al, 1991) through the entire Z depth. Accordingly, neuron cell bodies were counted in a subsample of sections, section thicknesses, and section areas, and then the results were extrapolated to provide estimates of total number of TH+ neurons. Contours (virtual outlines) of the SNpc were drawn in Stereo Investigator (MBF, Williston, VT) for each transverse histologic brain section. The SNpc region is a dense region of neuron soma which regresses along the pars reticulata (SNr). Areas not included in the tracings were the SNr, pars laterials, ventral tegmental area, and immunoreactive fiber projections from the pars compacta. Sampling parameters for stereological cell counting were established in the Optical Fractionator Workflow module as follows:
•Data Analysis
Neuronal cell counts were exported to individual Excel spreadsheet files and neuronal populations through the Z-depth layers were assessed to determine the uniformity of distribution. As anticipated, neuronal cell densities of were consistently non-uniform throughout the Z-depth; however, the relative pattern of distribution was fairly symmetrical from the Z-depth midline to the top and bottom surfaces of the tissue sections. To further understand the quantitative impact this biphasic distribution has on total population estimates, we then superimposed disectors at various depths and calculated estimates of TH+ neuron numbers using the optical fractionator formula.
Nobj = 1/ssf × 1/asf × 1/tsf × ΣQ
Nobj = number of objects
ssf (section sampling fraction) = the number of SNpc sections evaluated / total number of SNpc sections microtomed
asf (area sampling fraction) = counting frame size (microns) / counting frame interval (microns)
tsf (thickness sampling fraction) = disector height (microns) / mean measured section thickness (microns)
ΣQ = the number of cells counted
Counting Frame Area (XY) (μm²)
36,00
Disector Height (Z) (μm) 1
19.0
Disector Volume (XYZ) (μm³)
68,400
Guard Zone Distance (μm)
0.0
Sampling Grid Area (XY) (μm²)
14,400
Section Evaluation Interval
3
Mean Estimates of TH+ Neurons Produced by the Optical Fractionator Probe (N = 20)
Total Markers
Number of Sections
Number of Sampling Sites
Measured Defined Mounted Thickness
Estimated Population by Mean Section Thickness
C.E. Values (m=1)
680
(± 133.79)
13
(± 1.10)
385
(± 26.64)
15.93
(± 0.99)
6,859
(± 1,476)
0.05
(± 0.01)
0
20
40
60
80
100
120
0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
-15
-16
-17
-18
No. of TH+ Neurons
Z Depth from top of Section (-1.00μm)
Mean Z-Depth Distribution of
TH+ Neurons
7,795
14,229
2,680
7,628
9,178
8,953
8,729
8,716
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
Disector
0-13
microns
Disector
2-5
microns
Disector
6-11
microns
Disector
4-13
microns
Disector
2-8
microns
Disector
2-15
microns
Disector
9-15
microns
Baquet
et al.,
2009
Estimate No. of TH+ Neurons
Dissecting the Disector: Optical Fractionator Estimates of Total TH+ Neurons Relative to Disector Placement
This distribution illustrates the non-uniform density of TH+ neurons as counted throughout the Z-depth. Note the symmetry of the biphasic pattern.
Potential reasons for this biphasic distribution as described frequently in the literature include problems of antibody/stain penetration, differential tissue shrinkage, and differential tissue compression (Gardella et al., 2003), (Torres et al., 2006), (Carlo et al., 2011).
To further understand the underlying cause of this non-uniform TH+ distribution along the Z-depth, we examined a representative tissue section from a unique perspective.
A coronal 40 μm free-floating brain section containing the SNpc was immunostained as previously described, minus the cresyl violet counterstain. The immunostained section was then rotated perpendicularly and embedded on edge in optimal cutting temperature compound (O.C.T., Sakura Finetek). Cryosections were obtained transversely (i.e, sagittal plane) at 10 μm intervals through the region of the SNpc and counterstained in FD Cresyl Violet as previously described.
•(A) Stereo Investigator 20X Virtual Slice image of immunoreactivity through the sagittal plane (Z-Depth) of a 40 μm coronal section.
•(B) 100X image of the SNpc. The pattern of immunoreactivity correlates with the counted mean Z-depth distribution of TH+ neurons. Non-immunoreactive cells (cresyl violet) were observed infrequently in the middle portion of the z-depth.
•(C) Region distal to the SNpc. Immunoreactive neuronal processes are clearly visible throughout the Z-depth. Non-immunoreactive cells were distributed evenly throughout.
A
B
C
Disectors of varying heights were superimposed at different sampling depths within the extrapolated mean Z-depth distributions. To determine total population estimates relative to each disector placement, estimates of TH+ neuron numbers were calculated using the optical fractionator formula.
Disectors that encompassed symmetrical portions of the bisphasic curve produced numbers that were comparable to previous estimates of TH+ neurons in C57BL/6J mice: 8716 ±338 (range = 7546-9869, N = 10) (Baquet et al., 2009)
RESULTS