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Laser Capture Microdissection

Laser Capture Microdissection is a powerful technique that allows researchers to isolate and extract specific cells or tissues from a heterogeneous sample with high precision.
This method uses a laser to selectively remove target cells, enabling in-depth molecular analysis and the study of complex biological processes.
Coupled with advanced analytical tools, Laser Capture Microdissection has become an invaluable tool for a wide range of applications, including cancer research, stem cell biology, and neuroscience.
By providing a means to isolate pure populations of cells, this technqiue enhances reproducibility and enables researchers to gain novel insights into the mechanisms underlying health and disease.

Most cited protocols related to «Laser Capture Microdissection»

Due to the overwhelming sparsity in microbiome datasets, some filtering is required in order to infer microbe-metabolite interactions. We chose to filter out microbes that appear in less than 10 samples, since these microbes don’t have enough information to infer which metabolites are co-occurring with them. In other words the mmvec model has too many degrees of freedom to perform inference on these microbes. For the cystic fibrosis study, there were 172 samples and after filtering there were 138 unique microbial taxa and 462 metabolite features. For the biocrust soils study, there were 19 samples and after filtering there were 466 unique microbial taxa and 85 metabolite features. For the murine high fat diet study, there were 434 samples and after filtering there were 902 microbes and 11978 metabolites. For the IBD dataset, there were 13920 features in the c18 LCMS dataset, 26966 features in the c8 LCMS dataset and 562 taxa. Cross validation was performed across all studies to evaluate overfitting. In the desert biocrust soils experiment, 1 sample out of 19 samples was randomly chosen to be left out for cross-validation. In all of the other studies, 10 samples were randomly chosen to be left out for cross-validation. All of the analyses can be found under https://github.com/knightlab-analyses/multiomic-cooccurences.
Publication 2019
Cystic Fibrosis Diet, High-Fat Laser Capture Microdissection Microbial Interactions Microbiome Mus

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Publication 2011
Freezing Frozen Sections Gene Expression Intestinal Polyps Laser Capture Microdissection Medical Devices MicroRNAs Mus Polyps SMAD4 protein, human Tamoxifen Tissues

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Publication 2018
Biopsy BLOOD Bones Core Needle Biopsy Dreams Ethics Committees, Research Freezing Germ Line Homo sapiens Laser Capture Microdissection Males Neoplasm Metastasis Neoplasms Patients Prostate Cancer Tissues
The four GC methods used to predict functional interactions among E. coli proteins were based on: (1) functional linkages among genes which fuse to form a single open reading frame in at least one other genome, i.e., gene fusion [48 (link)]; (2) the mutual information of the coordinated presence or absence of pairs of genes across a set of 440 nonredundant genomes, i.e., phylogenetic profiles [51 (link),97 (link)]; and the natural chromosomal association of bacterial genes in operons as detected by two alternative methods, namely (3) the tendency of genes forming operons to show small intergenic distances [98 (link),99 ], and (4) the conservation of gene order, in which a confidence value for each pair of adjacent genes in the same strand was used as indicator that those genes likely form an operon, as compared with the conservation of adjacent genes in opposite strands [53 (link)]. For the last two methods, subsequent operon rearrangements were detected by genomic mapping of orthologs across 440 nonredundant bacterial genomes [55 (link)].
For all four GC methods, we used the BLAST-BDBHs as an operational definition of orthology (see Protocol S5 for details). To avoid circularity, the prediction scores of the four GC methods were benchmarked separately using proteins belonging to the same metabolic pathway according to EcoCyc [11 (link)] as positive reference set, and proteins in different pathways as negatives (Protocol S5). A single, unified high-confidence functional association network was then constructed by integrating the interaction predictions generated by the four GC methods using the same scoring model [61 (link)] used to integrate the MALDI and LCMS data (Protocol S6).
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Publication 2009
Chromosomes Escherichia coli Proteins Gene Fusion Gene Order Gene Rearrangement Genes Genes, Bacterial Genes, vif Genome Genome, Bacterial Laser Capture Microdissection Linkage, Genetic Operon Proteins Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Detailed methods are provided in the Supplemental Material. The data are available through https://cics.bwh.harvard.edu/multiomics_databases11 .
In total, 25 AVs were used in this study. AV leaflets were obtained from AV replacement surgeries for severe AV stenosis (Brigham and Women’s Hospital (BWH) approved IRB protocol number: 2011P001703). Written informed consent was provided. In brief, human stenotic AVs were segmented into stages of disease progression: (1) non-diseased, (2) fibrotic, and (3) calcific under the guidance of near-infrared molecular imaging. Transition zones were excluded from all analyses. In total, 27 sub-samples were prepared for label-free proteomics and 9 for transcriptomics.
AVs obtained from three additional patients with severe aortic valve stenosis were used for tissue layer tandem mass tagging (TMT) proteomics and AVs from autopsy donors served as controls. Anatomical layer-specificity was facilitated by laser capture microdissection.
Side-specific in vitro layer calcification potential was evaluated through a migration assay on AV leaflets from eight additional patients with severe AV stenosis after inspection by a pathologist to distinguish the fibrosa from the ventricularis side, and calcification was assessed by Alizarin Red staining at day 21. All cells which underwent proteomics were cultured and passaged in vitro prior to protein collection.
AV whole tissue label-free peptide samples were examined with the Q Exactive mass spectrometer. AV tissue layer TMT and in vitro migration label-free peptide samples were analyzed with the LTQ-Orbitrap Elite mass spectrometer.
For pathway analysis, the protein sets corresponding to each layer and stage were tested for enrichment by a hypergeometric test and adjusted for multiple comparisons using the Benjamini-Hochberg method for controlling the false discovery rate (FDR). Pathway networks were constructed based on their gene overlap. Layer- and stage-specific subnetworks were generated from literature-curated physical protein interactions. The closeness of the calcific stage subnetwork to human diseases was evaluated using average shortest network distance.
Publication 2018
Aortic Valve Stenosis Autopsy Calcinosis Cell Migration Assays Cells CIC protein, human Disease Progression Donors Fibrosis Gene Expression Profiling Genes Homo sapiens Laser Capture Microdissection Operative Surgical Procedures Pathologists Patients Peptides Physical Examination Proteins Stenosis Tissues Woman

Most recents protocols related to «Laser Capture Microdissection»

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Example 7

LC-ESI-HRMS Method for the Determination of Varenicline Nitroso-Drug Substance Related Impurity, U.S. FDA, Aug. 6, 2021, www.fda.gov/media/151470/download (accessed Feb. 27, 2022) can be used to test for impurities.

Other analytical methods for the entire process can be used to test for impurities. The methods are validated as per ICH guideline.

For example, HPLC RS methods are used for quantifying the impurities. The nitrosamines, monomethyl and dimethyl tartaric acid esters are quantified by LCMS methods.

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Patent 2024
Esters High-Performance Liquid Chromatographies Laser Capture Microdissection Nitrosamines Pharmaceutical Preparations tartaric acid Varenicline

Example 64

A 1:100 back-dilution from overnight culture of SYN-PKU-2002 was grown to early log phase for 1.5 h before moving to the anaerobic chamber for 4 hours in the presence of 1 mM IPTG and 0.1% arabinose for induction as described herein. To perform activity assay, 1e8 cells were resuspended and incubated in assay buffer (M9 media with 0.5% glucose, 50 mM Phe, and 50 mM MOPS with 50 mM phenylalanine). Supernatant samples were taken over time and TCA (the product of PAL) was measured by absorbance at 290 nm to determine the rate of TCA production/PAL activity. Phenylpyruvate was measured using LCMS methods described herein. Results are shown in FIG. 16A and FIG. 16B.

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Patent 2024
3-phenylpyruvate Arabinose Biological Assay Buffers Cells Glucose Isopropyl Thiogalactoside Laser Capture Microdissection morpholinopropane sulfonic acid Phenylalanine TCL1B protein, human Technique, Dilution

Example 164

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Step 1.

5-(7-Methylpyrazolo[1,5-a]pyridin-6-yl)spiro[3H-benzofuran-2,4′-piperidine] 2HCl was synthesized using tert-butyl 5-(7-methylpyrazolo[1,5-a]pyridin-6-yl)spiro[3H-benzofuran-2,4′-piperidine]-1′-carboxylate and 4 M HCl in dioxane. Analysis: LCMS m/z=320 (M+1); 1H NMR (400 MHz, DMSO-d6) δ: 8.94 (dd, J=4.0, 1.8 Hz, 1H), 8.38 (dd, J=8.3, 1.8 Hz, 1H), 7.75 (d, J=8.8 Hz, 1H), 7.57 (d, J=8.5 Hz, 1H), 7.55-7.52 (m, 1H), 7.47 (d, J=1.5 Hz, 1H), 7.38 (dd, J=8.3, 2.0 Hz, 1H), 6.88 (d, J=8.3 Hz, 1H), 6.01 (s, 2H), 3.91 (s, 3H), 3.57-3.39 (m, 4H), 3.12 (s, 2H), 1.86-1.67 (m, 4H).

Step 2.

5-(7-Methylpyrazolo[1,5-a]pyridin-6-yl)spiro[3H-benzofuran-2,4′-piperidine]-1′-carboxamide was synthesized using 5-(7-methylpyrazolo[1,5-a]pyridin-6-yl)spiro[3H-benzofuran-2,4′-piperidine] 2HCl and trimethylsilyl isocyanate. Analysis: LCMS m/z=363 (M+1); 1H NMR (400 MHz, DMSO-d6) δ: 8.05 (d, J=2.3 Hz, 1H), 7.64 (d, J=8.8 Hz, 1H), 7.27 (d, J=1.5 Hz, 1H), 7.16 (d, J=8.8 Hz, 2H), 6.86 (d, J=8.3 Hz, 1H), 6.68 (d, J=2.3 Hz, 1H), 6.01 (s, 2H), 3.53-3.37 (m, 4H), 3.10 (s, 2H), 2.65 (s, 3H), 1.83-1.67 (m, 4H).

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Patent 2024
1H NMR benzofuran Dioxanes Isocyanates Laser Capture Microdissection piperidine Sulfoxide, Dimethyl TERT protein, human

Example 190

[Figure (not displayed)]

Analysis: LCMS (ESI): 399 (M+H); 1H NMR (400 MHz, methanol-d4) δ 9.13 (d, J=2.3 Hz, 1H), 8.62-8.52 (m, 1H), 8.09-7.98 (m, 2H), 7.85-7.71 (m, 3H), 7.7-7.64 (m, 1H), 7.47-7.34 (m, 2H), 5.59-5.46 (m, 1H), 4.73-4.69 (m, 1H), 4.18-3.77 (m, 4H), 3.74-3.52 (m, 2H), 3.43 (s, 2H), 2.26-1.85 (m, 6H).

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Patent 2024
1H NMR Laser Capture Microdissection Methanol tetrahydrofuran

Example 146

[Figure (not displayed)]

This compound was synthesized using CDI, O-(tetrahydro-2H-pyran-2-yl)hydroxylamine, and 6-(3-isoquinolyl)spiro[chromane-2,4′-piperidine] TFA salt. Analysis: LCMS m/z=474 (M+1); 1H NMR (400 MHz, CDCl3) δ: 9.30 (s, 1H), 8.00-7.95 (m, 2H), 7.92 (d, J=2.3 Hz, 1H), 7.88-7.82 (m, 2H), 7.68 (td, J=7.6, 1.1 Hz, 1H), 7.58-7.52 (m, 1H), 7.30 (s, 1H), 6.97 (d, J=8.5 Hz, 1H), 5.01-4.84 (m, 1H), 4.02-3.91 (m, 1H), 3.90-3.78 (m, 2H), 3.71-3.57 (m, 1H), 3.41-3.26 (m, 2H), 2.91 (t, J=6.8 Hz, 2H), 1.95-1.76 (m, 7H), 1.71-1.53 (m, 5H).

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Patent 2024
1H NMR Hydroxylamine Laser Capture Microdissection piperidine Pyrans Salts

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More about "Laser Capture Microdissection"

Laser Capture Microdissection (LCM) is a powerful analytical technique that enables researchers to precisely isolate and extract specific cells or tissue samples from a heterogeneous population.
This method utilizes a laser to selectively remove target cells, facilitating in-depth molecular analysis and the study of complex biological processes.
LCM is widely used in a variety of applications, including cancer research, stem cell biology, and neuroscience.
By providing a means to isolate pure populations of cells, this technique enhances reproducibility and enables researchers to gain novel insights into the mechanisms underlying health and disease.
LCM can be coupled with advanced analytical tools, such as mass spectrometry (e.g., LCMS-2020, LCMS-IT-TOF, LCMS-8050, LCMS-8040, LCMS-8060, LCMS-IT-TOF) and RNA extraction kits (e.g., RNeasy Micro Kit), to facilitate comprehensive molecular profiling and characterization of the isolated cells.
The precision and selectivity of LCM make it an invaluable tool for researchers, allowing them to focus their analysis on specific cell types or tissues of interest.
This technique has become indispensable in fields like oncology, where the ability to isolate and analyze tumor cells can lead to a better understanding of cancer progression and the development of targeted therapies.
In addition to its scientific applications, LCM has also found use in forensic investigations, where it can be used to isolate and analyze trace evidence from complex samples.
Overall, Laser Capture Microdissection represents a powerful and versatile technique that continues to drive advancements in our understanding of biological systems and disease processes.
By combining LCM with other analytical tools, researchers can unlock new insights and accelerate the pace of scientific discovery.