3d Confocal Microscopy and Image Analysis for Measurement of Genetic Instability

1999 ◽  
Vol 5 (S2) ◽  
pp. 1022-1023
Author(s):  
C. Ortiz de Solorzano ◽  
K. Chin ◽  
D. Knowles ◽  
A. Jones ◽  
E. Garcia ◽  
...  
Keyword(s):  
Image Analysis ◽  
Confocal Microscopy ◽  
Genetic Instability ◽  
Copy Number ◽  
3D Image ◽  
3D Images ◽  
Tissue Sections ◽  
Total Dna ◽  
Natural Tissue

Solid tumors frequently contain cells that are heterogeneous in the copy number of DNA loci. This fact implies the existence of genetic instability, which may be associated with disease aggressiveness. Accurate measurement of this phenomenon requires analysis of intact nuclei within their natural tissue context. We perform these measurements by preparing >30μm thick tissue sections, labeling them with fluorescent labels for total DNA and for specific DNA loci using fluorescence in situ hybridization (FISH) which retain the transparency of the tissue and acquiring 3D images of the tissue using confocal microscopy (figure 1). In this study, we combined automated 3D image analysis (IA) algorithms for segmenting individual nuclei based on the total DNA stain1 and for segmenting the punctuate FISH signals of DNA loci. This enables us to efficiently enumerate the copy number of specific DNA loci in individual cells and as a function of the cell's location in the tissue.

Multiple Iteractive Labeling by Antibody Neodeposition (MILAN)

2019 ◽  
Author(s):  
Giorgio Cattoretti ◽  
Francesca Maria Bosisio ◽  
Lukas Marcelis ◽  
Maddalena Maria Bolognesi
Keyword(s):  
Image Analysis ◽  
Tissue Section ◽  
Indirect Immunofluorescence ◽  
Analysis Software ◽  
Tissue Sections ◽  
Image Analysis Software ◽  
Single Section ◽  
Fluorescent Image ◽  
Secondary Antibodies

Abstract Multiplexing, labeling for multiple immunostains the very same cell or tissue section in situ, is of considerable interest. The major obstacles to the diffusion of this technique are high costs in custom antibodies and instruments, low throughput, scarcity of specialized skills or facilities. We have validated and detail here a method based on common primary and secondary antibodies, diffusely available fluorescent image scanners and routinely processed tissue sections \(FFPE). It entails rounds of four-color indirect immunofluorescence, image acquisition and removal \(stripping) of the antibodies, before another stain is applied. The images are digitally registered and the autofluorescence is subtracted. Removal of antibodies is accomplished by disulphide cleavage. In excess of 50 different antibody stains can be applied to one single section from routinely fixed and embedded tissue. This method requires a modest investment in hardware and materials and uses freeware image analysis software.

scholarly journals Measurement of Capillary Length from 3D Images Acquired by Confocal Microscopy Using Image Analysis and Stereology

2010 ◽  
Vol 16 (S2) ◽  
pp. 736-737
Author(s):  
L Kubínová ◽  
J Janáček ◽  
I Eržen ◽  
XW Mao
Keyword(s):  
Image Analysis ◽  
Confocal Microscopy ◽  
Capillary Length ◽  
3D Images

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.

scholarly journals frenchFISH: Poisson models for quantifying DNA copy-number from fluorescence in situ hybridisation of tissue sections

2018 ◽  
Author(s):  
Geoff Macintyre ◽  
Anna M Piskorz ◽  
Edith Ross ◽  
David B Morse ◽  
Ke Yuan ◽  
...  
Keyword(s):  
Copy Number ◽  
Process Model ◽  
In Situ Hybridisation ◽  
Poisson Model ◽  
Correction Method ◽  
Fluorescence In Situ Hybridisation ◽  
Dna Copy Number ◽  
Tissue Sections ◽  
Poisson Models

Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. Imaging of spots generated using fluorescence in situ hybridisation (FISH) of locus specific probes is routinely used to detect copy number changes in tumour nuclei. However, it often does not perform well on solid tumour tissue sections, where partially represented or overlapping nuclei are common. To overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. We benchmarked the performance of FrenchFISH against previous approaches in a controlled simulation scenario and exemplify its use in 12 ovarian cancer FFPE-tissue sections, for which we assess copy number alterations in three loci (c-Myc, hTERC and SE7). We show that FrenchFISH outperforms standard spot counting approaches and that the automated spot counting is significantly faster than manual without loss of performance. FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue.

scholarly journals FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections

2021 ◽  
pp. 176-186
Author(s):  
Geoff Macintyre ◽  
Anna M. Piskorz ◽  
Adam Berman ◽  
Edith Ross ◽  
David B. Morse ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Fluorescence In Situ Hybridization ◽  
Copy Number ◽  
Process Model ◽  
Poisson Model ◽  
Correction Method ◽  
Dna Copy Number ◽  
Tissue Sections ◽  
Poisson Models

PURPOSE Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. The gold standard for detecting copy number changes in tumor cells is fluorescence in situ hybridization (FISH) using locus-specific probes that are imaged as fluorescent spots. However, spot counting often does not perform well on solid tumor tissue sections due to partially represented or overlapping nuclei. MATERIALS AND METHODS To overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes or a homogeneous Poisson point process model for automated spot counting. RESULTS We benchmarked the performance of FrenchFISH against previous approaches using a controlled simulation scenario and tested it experimentally in 12 ovarian carcinoma FFPE-tissue sections for copy number alterations at three loci (c-Myc, hTERC, and SE7). FrenchFISH outperformed standard spot counting with 74% of the automated counts having < 1 copy number difference from the manual counts and 17% having < 2 copy number differences, while taking less than one third of the time of manual counting. CONCLUSION FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue by both speeding up and improving the accuracy of spot count estimates.

scholarly journals Multiple Iterative Labeling by Antibody Neodeposition (MILAN)

2019 ◽  
Author(s):  
Giorgio Cattoretti ◽  
Francesca Maria Bosisio ◽  
Lukas Marcelis ◽  
Maddalena Maria Bolognesi
Keyword(s):  
Image Analysis ◽  
Tissue Section ◽  
Indirect Immunofluorescence ◽  
Analysis Software ◽  
Tissue Sections ◽  
Image Analysis Software ◽  
Single Section ◽  
Fluorescent Image ◽  
Secondary Antibodies

Abstract Multiplexing, labeling for multiple immunostains the very same cell or tissue section in situ, is of considerable interest. The major obstacles to the diffusion of this technique are high costs in custom antibodies and instruments, low throughput, scarcity of specialized skills or facilities. We have validated and detail here a method based on common primary and secondary antibodies, diffusely available fluorescent image scanners and routinely processed tissue sections \(FFPE). It entails rounds of four-color indirect immunofluorescence, image acquisition and removal \(stripping) of the antibodies, before another stain is applied. The images are digitally registered and the autofluorescence is subtracted. Removal of antibodies is accomplished by disulphide cleavage. In excess of 50 different antibody stains can be applied to one single section from routinely fixed and embedded tissue. This method requires a modest investment in hardware and materials and uses freeware image analysis software.

scholarly journals Multiple Iterative Labeling by Antibody Neodeposition (MILAN)

2019 ◽  
Author(s):  
Giorgio Cattoretti ◽  
Francesca Maria Bosisio ◽  
Lukas Marcelis ◽  
Maddalena Maria Bolognesi
Keyword(s):  
Image Analysis ◽  
Tissue Section ◽  
Indirect Immunofluorescence ◽  
Analysis Software ◽  
Tissue Sections ◽  
Image Analysis Software ◽  
Single Section ◽  
Fluorescent Image ◽  
Secondary Antibodies

Abstract (What’s new in protocol Version 5: an expanded troubleshooting section, more validated antibodies)Multiplexing, labeling for multiple immunostains the very same cell or tissue section in situ, is of considerable interest. The major obstacles to the diffusion of this technique are high costs in custom antibodies and instruments, low throughput, scarcity of specialized skills or facilities. We have validated and detail here a method based on common primary and secondary antibodies, diffusely available fluorescent image scanners and routinely processed tissue sections \(FFPE). It entails rounds of four-color indirect immunofluorescence, image acquisition and removal \(stripping) of the antibodies, before another stain is applied. The images are digitally registered and the autofluorescence is subtracted. Removal of antibodies is accomplished by disulphide cleavage. In excess of 50 different antibody stains can be applied to one single section from routinely fixed and embedded tissue. This method requires a modest investment in hardware and materials and uses freeware image analysis software.

scholarly journals Spatially-resolved in-situ quantification of biofouling using optical coherence tomography (OCT) and 3D image analysis in a spacer filled channel

2017 ◽  
Vol 524 ◽  
pp. 673-681 ◽  
Author(s):  
Luca Fortunato ◽  
Szilárd Bucs ◽  
Rodrigo Valladares Linares ◽  
Corrado Cali ◽  
Johannes S. Vrouwenvelder ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Optical Coherence ◽  
3D Image ◽  
3D Image Analysis ◽  
Spatially Resolved
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