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Ravindran, A.

Publications and source records attributed to Ravindran, A..

2 recordsLinked to original sources

Immunoprofiling reveals novel mast cell receptors and a continuous nature of human lung mast cell heterogeneity

BackgroundImmunohistochemical analysis of granule-associated proteases have revealed that human lungs mast cells constitute a heterogeneous population of cells, with distinct subpopulations identified. However, a systematic and comprehensive analysis of cell surface markers to study human lung mast cell heterogeneity is yet to be performed. MethodsHuman lung mast cells were obtained from lung lobectomies and the expression of 332 cell surface markers were analyzed using flow cytometry and the LEGENDScreen kit. Markers that exhibited a high variance were selected for additional analyses to reveal whether they correlated and if discrete mast cell subpopulations were discernable. ResultsWe identified expression of 102 surface markers on human lung mast cells. Several markers showed a high continuous variation of expression within the mast cell population. Six of these markers correlated: SUSD2, CD49a, CD326, CD34, CD66 and HLA-DR. The expression of these markers also correlated to the size and granularity of the mast cells. However, no marker produced an expression profile consistent with a bi- or multimodal distribution. ConclusionsLEGENDScreen analysis identified more than 100 cell surface markers on mast cells, out of which 23 have to our knowledge not previously described on human mast cells. Several of the newly described markers are known to be involved in sensing the microenvironment, and their identification can shed new light on mast cell functions. The exhaustive expression profiling of the 332 surface markers failed to detect distinct mast cell subpopulations. Instead, we demonstrate a continuous nature of human lung mast cell heterogeneity.

immunology

BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data

Modern bioimaging and related areas such as sensor technology has seen tremendous development the last years allowing several contemporary imaging techniques, electron microscopy (EM) and light sheet microscopy in particular, to generate datasets frequently reaching the size of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2 - a Fiji plugin facilitating processing workflows for TB sized image datasets. Availability and implementationBigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2). Contactchristian.tischer@embl.de, nils.norlin@med.lu.se

bioinformatics