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Mast cell accumulation is a hallmark of a number of diseases, including allergic asthma and systemic mastocytosis. Immunoglobulin E–mediated crosslinking of the FcεRI receptors causes mast cell activation and contributes to disease pathogenesis. The mast cell lineage is one of the least studied among the hematopoietic cell lineages, and controversies remain about whether FcεRI expression appears during the mast cell progenitor stage or during terminal mast cell maturation. Here, we used single-cell transcriptomics analysis to reveal a temporal association between the appearance of FcεRI and the mast cell gene signature in CD34+ hematopoietic progenitors in adult peripheral blood. In agreement with these data, the FcεRI+ hematopoietic progenitors formed morphologically, phenotypically, and functionally mature mast cells in long-term culture assays. Single-cell transcriptomics analysis further revealed the expression patterns of prospective cytokine receptors regulating development of mast cell progenitors. Culture assays showed that interleukin-3 (IL-3) and IL-5 promoted disparate effects on progenitor cell proliferation and survival, respectively, whereas IL-33 caused robust FcεRI downregulation. Taken together, we showed that FcεRI expression appears at the progenitor stage of mast cell differentiation in peripheral blood. We also showed that external stimuli regulate FcεRI expression of mast cell progenitors, providing a possible explanation for the variable FcεRI expression levels during mast cell development.

Background: Immunohistochemical analysis of granule-associated proteases has revealed that human lung 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 has yet to be performed. Methods: Human lung mast cells were obtained from lung lobectomies, and the expression of 332 cell-surface markers was analyzed using flow cytometry and the LEGENDScreen kit. Markers that exhibited high variance were selected for additional analyses to reveal whether they were correlated and whether discrete mast cell subpopulations were discernable. Results: We identified the expression of 102 surface markers on human lung mast cells. Several markers showed high continuous variation in expression within the mast cell population. Six of these markers were correlated: SUSD2, CD49a, CD326, CD34, CD66 and HLA-DR. The expression of these markers was also correlated with the size and granularity of mast cells. However, no marker produced an expression profile consistent with a bi- or multimodal distribution. Conclusions: LEGENDScreen analysis identified more than 100 cell-surface markers on mast cells, including 23 that, to the best of our knowledge, have not been 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 the continuous nature of human lung mast cell heterogeneity.

The nematode Caenorhabditis elegans has become an essential model organism in neuroscience research because of its stereotyped anatomy, relevance to human biology, and capacity for genetic manipulation. To solve the intrinsic challenges associated with performing manual operations on C. elegans, many automated chip designs based on immobilization-imaging-release approaches have been proposed. These designs are prone to limitations such as the exertion of physical stress on the worms and limited throughput. In this work, a continuous-flow, high-throughput, automated C. elegans analyzer based on droplet encapsulation and real-time image processing was developed to analyze fluorescence expression in worms. To demonstrate its capabilities, two strains of C. elegans nematodes with different levels of expression of green fluorescent protein (GFP) were first mixed in a buffer solution. The worms were encapsulated in water-in-oil droplets to restrict random locomotion. The droplets were closely packed in a two-layer polydimethylsiloxane (PDMS) platform and were flowed through a narrow straight channel, in which a region of interest (ROI) was defined and continuously recorded by a frame acquisition device. Based on the number of pixels counted in the selected color range, our custom software analyzed GFP expression to differentiate between two strains with nearly 100% accuracy and a throughput of 0.5 seconds/worm.