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Mackerodt, J.

Publications and source records attributed to Mackerodt, J..

3 recordsLinked to original sources

Distinct transcriptional states of draining lymph node T cells following influenza vaccination in individuals with underrepresented ancestries

The transcriptional and cell repertoire changes of lymph node (LN) T cells following influenza vaccination have been under-investigated across different ethnicities. Draining (dLN) and non-draining (ndLN) axillary LN cells from 17 participants with genotypically varied African or Asian ancestry (ISRCTN13657999) were collected by fine needle aspiration (FNA) before, 5 days and 42 days after adjuvanted influenza vaccination and analysed by single-cell RNA-sequencing (scRNA-seq). The early dLN transcriptional profile and cellular dynamics were distinct from the ndLN; BACH2 expressing T cells were highly induced, exhibiting stem-like and unique quiescence signatures. Proliferating T follicular helper cells (Tfh) were enriched in dLN and Th1-like Tfh predominated with highly induced transcriptional changes. The BACH2+ T cell state and Th1-like Tfh cells, transcriptionally distinct from germinal centre (GC) T cells, were co-localised with LAMP3+ dendritic cells in the T cell zone of publicly available LN datasets, a region that was also distinct from GC Tfh niches. Together, these findings indicate that the induction of a robust, proliferative, and transcriptionally diverse Tfh response, alongside a discrete subset of BACH2+ T cells, represents a marked feature of the early T cell zone immune response to adjuvanted influenza vaccine in the dLN.

immunology↗

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↗