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Mackerodt, J.

Publications and source records attributed to Mackerodt, J..

2 recordsLinked to original sources

An end-to-end framework for Cell DIVE multiplexed imaging and spatial immune microenvironment analysis

This paper describes an end-to-end workflow for highly multiplexed fluorescence imaging with the Cell DIVE platform, allowing simultaneous detection of 40+ markers at single-cell resolution. Combining whole-slide multiplexed imaging with a dedicated analysis pipeline provides a powerful approach to investigate immune cell interactions with stromal and vascular networks within human tissue microenvironments. With a focus on spatial investigation of human immune niches, here we provide a complete framework for tissue preparation, autofluorescence reduction, multiplex panel design and whole-slide image analysis. For complete details on the use and execution of this protocol, please refer to Korsunsky et al. (Med, 2022) [1]. HighlightsO_LIComplete workflow for Cell DIVE multiplex imaging and quantitative image analysis. C_LIO_LIHuman FFPE tissue preparation, LED-based reduction of tissue autofluorescence. C_LIO_LIAntibody panel design for 3-40 marker multiplexing, in-house antibody conjugation. C_LIO_LIQuPath and DeepCell based analysis workflows for whole-slide multi-marker images. C_LIO_LIAdaptable code templates to accelerate cell segmentation and spatial niche analysis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/656440v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1ef708dorg.highwire.dtl.DTLVardef@c6422dorg.highwire.dtl.DTLVardef@22d961org.highwire.dtl.DTLVardef@1ed7479_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

The Impact of Model Assumptions in Interpreting Cell Kinetic Studies

Author summaryStable isotope labelling is one of the best available methods for quantifying cell dynamics in vivo, particularly in humans where the absence of toxicity makes it preferable over other techniques such as CFSE or BrdU. Interpretation of stable isotope labelling data necessitates simplifying assumptions. Here we investigate the impact of three of the most commonly used simplifying assumptions (that the cell population of interest is closed, that the population of interest is kinetically homogeneous, and that the population is spatially homogeneous) and suggest pragmatic ways in which the resulting errors can be reduced.

immunology↗