workflow  (Oxford Instruments)

Journal: STAR Protocols

Article Title: Protocol for 3D-guided sectioning and deep cell phenotyping via light sheet imaging and 2D spatial multiplexing

doi: 10.1016/j.xpro.2025.104296

Figure Lengend Snippet: 3D spatial and data analysis workflow 3D spatial analysis of 3D-IF stained and optically cleared samples with UltraMicroscope Blaze™ light sheet microscope (Step 16). Post processing (stitching) in case data was acquired with tile-scanning (Step 18). Surfaces generation of autofluorescence and target region with Imaris software (Oxford Instruments) and target plane definition with “Oblique Slicer” tool (Step 20 and 20).

Article Snippet: Continue the workflow in Imaris to create orientation marks for further analysis.

Techniques: Staining, Microscopy, Software

Journal: BMC Bioinformatics

Article Title: MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis

doi: 10.1186/s12859-025-06348-1

Figure Lengend Snippet: Effect of varying ppm error thresholds on peak assignments: Number of a monoisotopic and b fine-structure peak matches for peak lists from two technical replicates of a diatom sample (testdata1.asc and testdata1.asc) containing naturally abundant metabolites (blue/light blue bars) with a 13 C-labeled IROA-IS spike-in (orange/pink bars), when compared with a list of 8,529 unique chemical formulas for 16,089 distinct KEGG compounds ranging between 40–1000 Daltons. Comparisons were performed across a range of error thresholds against the theoretical masses of metabolites with either natural isotopic abundance (nat_nist) or 95 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} 13 C-labeling (C13_95). a ) Number of distinct molecular features (monoisotopic masses) identified at varying -p settings of 0.1, 0.5, 1 ppm, with -vp held constant at 0.5 ppm. b ) Average number of minor isotopic variants detected per matched chemical formula at a -p setting of 0.5 and varying -vp values of 0.1, 0.5, 1 ppm

Article Snippet: The expected 13 C-labeled IROA-IS spike-in composition of around 500-1000 KEGG compounds (https://www.iroatech.com/wp-content/uploads/2022/02/TruQuant-Yeast-Extract-QC-Workflow-Kit-USER-MANUAL_022022.pdf) [ ] also compares well with the 618 and 1140 matched features at 0.5 and 1 ppm, respectively.

Techniques: Labeling

Journal: BMC Bioinformatics

Article Title: MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis

doi: 10.1186/s12859-025-06348-1

Figure Lengend Snippet: Validation rates of chemical formula assignments for monoisotopic masses using relative peak heights of minor isotopic variants : Number of unique CFs with monoisotopic matches (light bars), minor isotope variant matches (medium bars), and validated formulas (dark bars) for two sample types when compared with KEGG compounds between 40–1000 Daltons. The nitrogen-containing IROA metabolite standards dataset (orange) contains 274 unique chemical formulas. The diatom sample (blue; testdata1) contains a mixture of natural and 95% 13 C-labeled isotopes. Comparisons were performed across a range of ppm error thresholds using MIMI’s --iso-validation option with a 30% tolerance for isotopic fine-structure peak height matching. a ) Number of unique CFs detected at varying -p settings of 0.1, 0.5, 1 ppm, with -vp held constant at 0.5 ppm. b ) Number of unique CFs detected at -p setting of 0.5 and varying -vp values of 0.1, 0.5, 1 ppm.

Article Snippet: The expected 13 C-labeled IROA-IS spike-in composition of around 500-1000 KEGG compounds (https://www.iroatech.com/wp-content/uploads/2022/02/TruQuant-Yeast-Extract-QC-Workflow-Kit-USER-MANUAL_022022.pdf) [ ] also compares well with the 618 and 1140 matched features at 0.5 and 1 ppm, respectively.

Techniques: Biomarker Discovery, Variant Assay, Labeling