Other tools to characterize cell-secreted products lack the quantitative resolution, throughput, and multiplexing of flow cytometry and do not directly link secretions to transcriptomic information. However, transcript levels do not account for various downstream processes after transcription, including RNA splicing, translation, post-translational modifications, enzymatic cleavage, or even storage of secreted proteins in secretory vesicles prior to secretion in response to a stimulus. Similarly, transcripts associated with secreted proteins may sometimes correlate to secretion 1, 2, 3. Furthermore, the presence of secreted proteins on or within the cell does not necessarily indicate that these proteins would have been secreted. Downsides of this permeabilization and fixation process include the loss of cell viability and loss of other intracellular molecules, such as mRNA, which limits downstream transcriptomic analysis. Forming pores in the cell is necessary for fluorescently-labeled antibodies to penetrate the intracellular space and bind to chemically fixed proteins. Intracellular proteins, including secreted proteins, can be analyzed, e.g., using intracellular cytokine staining, but through a destructive process that involves permeabilization and fixation of the cell. One possible method is by combining flow cytometry with intracellular staining to quantify intracellular production of secreted proteins in the context of other markers. Standard single-cell analysis tools cannot simultaneously assess external cellular information (e.g., secreted proteins) with cell surface and/or intracellular information. Linking these functional changes in the capacity for secretion of immunoglobulins to genetic/phenotypic profiles at the single-cell level can uncover the population heterogeneity and potential new cell states. In this process, B cells differentiate, undergoing significant phenotypic, morphological, and genetic changes. For example, one of the main roles of B cells is to respond to antigens with the production and secretion of large quantities of immunoglobulins targeting antigen epitopes. Organisms critically depend on the proteins or other factors which cells secrete into their environment that can act locally in a paracrine manner or systemically. Altogether, this method links quantity of secretion with single-cell sequencing (SEC-seq) and enables researchers to fully explore the links between genome and function, laying the foundation for discoveries in immunology, stem cell biology, and beyond. By using oligonucleotide-labeled antibodies we find that upregulation of pathways for protein localization to the endoplasmic reticulum and mitochondrial oxidative phosphorylation are most associated with high IgG secretion, and uncover surrogate plasma cell surface markers (e.g., CD59) defined by the ability to secrete IgG. Measurements using flow cytometry and imaging flow cytometry corroborate the association between IgG secretion and CD38/CD138. By accumulating secretions close to secreting cells held within cavity-containing hydrogel nanovials, we demonstrate workflows to analyze the amount of IgG secreted from single human B cells and link this information to surface markers and transcriptomes from the same cells. ResCor <- data.The secreted products of cells drive many functions in vivo however, methods to link this functional information to surface markers and transcriptomes have been lacking. This should solve "correlation coef is not calculated for each facet" problem. Next we loop over all facet options with for(i in seq_along(resCor$facets)) and store result in rescore. We plot main plot getting this variable in facet_wrap(~ get(inputFacet), ncol = 3). In shiny you'll probably have user input as input$facet here it's called inputFacet. P<- p+geom_text(x=0.9*max(df$hp, na.rm=TRUE),Ĭall facets through variable inputFacet, loop over this variable to calculate corr_enq and plot facets using variable name with get. Really appreciate it if anyone could kindly help with that. I could not figure out a way to achieve that. It seems the correlation coef is not calculated for each facet. When I plot it out, both plot have the same correlation number. I'm having issue to put correlation coefficient on my scatter plot after facet_wrap by another variable.īelow is the example I made using mtcars dataset for illustration purpose.
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