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Ivanov AS, Ershov PV, Molnar AA, Mezentsev YV, Kaluzhskiy LA, Yablokov EO, Florinskaya AV, Gnedenko OV, Medvedev AE, Kozin SA, Mitkevich VA, Makarov AA, Gilep AA, Luschik AY, Gaidukevich IV, Usanov SA. Direct molecular fishing in molecular partners investigation in protein–protein and protein–peptide interactions. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2016. [DOI: 10.1134/s1068162016010052] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Irwin DJ, Byrne MD, McMillan CT, Cooper F, Arnold SE, Lee EB, Van Deerlin VM, Xie SX, Lee VMY, Grossman M, Trojanowski JQ. Semi-Automated Digital Image Analysis of Pick's Disease and TDP-43 Proteinopathy. J Histochem Cytochem 2015; 64:54-66. [PMID: 26538548 DOI: 10.1369/0022155415614303] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 10/03/2015] [Indexed: 12/12/2022] Open
Abstract
Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick's disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes.
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Affiliation(s)
- David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG)
| | - Matthew D Byrne
- Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG)
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG)
| | - Felicia Cooper
- Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG)
| | - Steven E Arnold
- Center for Neurodegenerative Disease Research,Department of Pathology & Laboratory Medicine(DJI, MDB, FC, SEA, EBL, VMVD, VML, JQT)
| | - Edward B Lee
- Center for Neurodegenerative Disease Research,Department of Pathology & Laboratory Medicine(DJI, MDB, FC, SEA, EBL, VMVD, VML, JQT)
| | - Vivianna M Van Deerlin
- Center for Neurodegenerative Disease Research,Department of Pathology & Laboratory Medicine(DJI, MDB, FC, SEA, EBL, VMVD, VML, JQT)
| | - Sharon X Xie
- Department of Biostatistics and Epidemiology ,University of Pennsylvania Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (SXX)
| | - Virginia M-Y Lee
- Center for Neurodegenerative Disease Research,Department of Pathology & Laboratory Medicine(DJI, MDB, FC, SEA, EBL, VMVD, VML, JQT)
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG)
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research,Department of Pathology & Laboratory Medicine(DJI, MDB, FC, SEA, EBL, VMVD, VML, JQT)
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Neltner JH, Abner EL, Schmitt FA, Denison SK, Anderson S, Patel E, Nelson PT. Digital pathology and image analysis for robust high-throughput quantitative assessment of Alzheimer disease neuropathologic changes. J Neuropathol Exp Neurol 2012; 71:1075-85. [PMID: 23147505 PMCID: PMC3511606 DOI: 10.1097/nen.0b013e3182768de4] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Quantitative neuropathologic methods provide information that is important for both research and clinical applications. The technologic advancement of digital pathology and image analysis offers new solutions to enable valid quantification of pathologic severity that is reproducible between raters regardless of experience. Using an Aperio ScanScope XT and its accompanying image analysis software, we designed algorithms for quantitation of amyloid and tau pathologies on 65 β-amyloid (6F/3D antibody) and 48 phospho-tau (PHF-1)-immunostained sections of human temporal neocortex. Quantitative digital pathologic data were compared with manual pathology counts. There were excellent correlations between manually counted and digitally analyzed neuropathologic parameters (R² = 0.56-0.72). Data were highly reproducible among 3 participants with varying degrees of expertise in neuropathology (intraclass correlation coefficient values, >0.910). Digital quantification also provided additional parameters, including average plaque area, which shows statistically significant differences when samples are stratified according to apolipoprotein E allele status (average plaque area, 380.9 μm² in apolipoprotein E [Latin Small Letter Open E]4 carriers vs 274.4 μm² for noncarriers; p < 0.001). Thus, digital pathology offers a rigorous and reproducible method for quantifying Alzheimer disease neuropathologic changes and may provide additional insights into morphologic characteristics that were previously more challenging to assess because of technical limitations.
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Affiliation(s)
- Janna Hackett Neltner
- Department of Pathology and Laboratory Medicine, University of Kentucky, 800 Rose St, MS-115A, Lexington, KY 40536, USA.
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Clérin E, Wicker N, Mohand-Saïd S, Poch O, Sahel JA, Léveillard T. ℮-conome: an automated tissue counting platform of cone photoreceptors for rodent models of retinitis pigmentosa. BMC Ophthalmol 2011; 11:38. [PMID: 22185426 PMCID: PMC3271040 DOI: 10.1186/1471-2415-11-38] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 12/20/2011] [Indexed: 11/24/2022] Open
Abstract
Background Retinitis pigmentosa is characterized by the sequential loss of rod and cone photoreceptors. The preservation of cones would prevent blindness due to their essential role in human vision. Rod-derived Cone Viability Factor is a thioredoxin-like protein that is secreted by rods and is involved in cone survival. To validate the activity of Rod-derived Cone Viability Factors (RdCVFs) as therapeutic agents for treating retinitis Pigmentosa, we have developed e-conome, an automated cell counting platform for retinal flat mounts of rodent models of cone degeneration. This automated quantification method allows for faster data analysis thereby accelerating translational research. Methods An inverted fluorescent microscope, motorized and coupled to a CCD camera records images of cones labeled with fluorescent peanut agglutinin lectin on flat-mounted retinas. In an average of 300 fields per retina, nine Z-planes at magnification X40 are acquired after two-stage autofocus individually for each field. The projection of the stack of 9 images is subject to a threshold, filtered to exclude aberrant images based on preset variables. The cones are identified by treating the resulting image using 13 variables empirically determined. The cone density is calculated over the 300 fields. Results The method was validated by comparison to the conventional stereological counting. The decrease in cone density in rd1 mouse was found to be equivalent to the decrease determined by stereological counting. We also studied the spatiotemporal pattern of the degeneration of cones in the rd1 mouse and show that while the reduction in cone density starts in the central part of the retina, cone degeneration progresses at the same speed over the whole retinal surface. We finally show that for mice with an inactivation of the Nucleoredoxin-like genes Nxnl1 or Nxnl2 encoding RdCVFs, the loss of cones is more pronounced in the ventral retina. Conclusion The automated platform ℮-conome used here for retinal disease is a tool that can broadly accelerate translational research for neurodegenerative diseases.
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Samaroo HD, Opsahl AC, Schreiber J, O'Neill SM, Marconi M, Qian J, Carvajal-Gonzalez S, Tate B, Milici AJ, Bales KR, Stephenson DT. High throughput object-based image analysis of β-amyloid plaques in human and transgenic mouse brain. J Neurosci Methods 2011; 204:179-188. [PMID: 22019329 DOI: 10.1016/j.jneumeth.2011.10.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Revised: 10/05/2011] [Accepted: 10/07/2011] [Indexed: 01/30/2023]
Abstract
Advances in imaging technology have enabled automated approaches for quantitative image analysis. In this study, a high content object based image analysis method was developed for quantification of β-amyloid (Aβ) plaques in postmortem brains of Alzheimer's disease (AD) subjects and in transgenic mice over overexpressing Aβ. Digital images acquired from immunohistochemically stained sections of the superior frontal gyrus were analyzed for Aβ plaque burden using a Definiens object-based segmentation approach. Blinded evaluation of Aβ stained sections from AD and aged matched human subjects accurately identified AD cases with one exception. Brains from transgenic mice overexpressing Aβ (PS1APP mice) were also evaluated by our Definiens object based image analysis approach. We observed an age-dependent increase in the amount of Aβ plaque load that we quantified in both the hippocampus and cortex. From the contralateral hemisphere, we measured the amount of Aβ in brain homogenates biochemically and observed a significant correlation between our biochemical measurements and those that we measured by our object based Definiens system in the hippocampus. Assessment of Aβ plaque load in PS1APP mice using a manual segmentation technique (Image-Pro Plus) confirmed the results of our object-based image analysis approach. Image acquisition and analysis of 32 stained human slides and 100 mouse slides were executed in 8 h and 22 h, respectively supporting the relatively high throughput features of the Definiens platform. The data show that digital imaging combined with object based image analysis is a reliable and efficient approach to quantifying Aβ plaques in human and mouse brain.
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Affiliation(s)
- Harry D Samaroo
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Alan C Opsahl
- Investigative Pathology, Pfizer Global Research & Development, USA
| | - Jan Schreiber
- Definiens AG, Trappentreustrasse 1, 80339 München, Germany
| | - Sharon M O'Neill
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Michael Marconi
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Jessie Qian
- Investigative Pathology, Pfizer Global Research & Development, USA
| | - Santos Carvajal-Gonzalez
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Barbara Tate
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Anthony J Milici
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
| | - Kelly R Bales
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA.
| | - Diane T Stephenson
- Neuroscience Biology, Pfizer Global Research & Development, Eastern Point Road, Groton, CT 06340, USA
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Faretta M. Automation in Multidimensional Fluorescence Microscopy. NANOSCOPY AND MULTIDIMENSIONAL OPTICAL FLUORESCENCE MICROSCOPY 2010:14-1-14-21. [DOI: 10.1201/9781420078893-c14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Abstract
Although all multicellular organisms undergo structural and functional deterioration with age, senescence is not a uniform process. Rather, each organism experiences a constellation of changes that reflect the heterogeneous effects of age on molecules, cells, organs and systems, an idiosyncratic pattern that we refer to as mosaic aging. Varying genetic, epigenetic and environmental factors (local and extrinsic) contribute to the aging phenotype in a given individual, and these agents influence the type and rate of functional decline, as well as the likelihood of developing age-associated afflictions such as cardiovascular disease, arthritis, cancer, and neurodegenerative disorders. Identifying key factors that drive aging, clarifying their activities in different systems, and in particular understanding how they interact will enhance our comprehension of the aging process, and could yield insights into the permissive role that senescence plays in the emergence of acute and chronic diseases of the elderly.
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Affiliation(s)
- Lary C Walker
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.
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