UL86 is highly conserved, and it shares similarities with the major capsid proteins of Epstein-Barr virus, herpes simplex virus type 1, varicellazoster virus, and human herpes virus 6. Infectious virions contain a large proportion of UL86 (∼6%) in a molar ratio of approx. Along with 4 other viral proteins (UL85, UL80, UL48/49, UL46), UL86 forms the icosahedral capsid of infectious virions and noninfectious enveloped particles. study is the only one that has explored this aspect of UL86. Of 33 healthy CMV-exposed individuals, 22 (66%) had a CD4 T cell response to the 1370-amino acid major capsid protein UL86, which makes it the third most-recognized CD4 T cell target in this cohort. recently showed that up to 140 CMV proteins are recognized by the T cell response. Only a few studies provided data on the magnitude and characteristics of CMV-specific CD4 T cells. Until recently, most studies focused on just a few of these, in particular on pp65 and IE-1 and CD8 T cells.
CMV's genome contains ∼200 open reading frames (ORFs), each of which potentially codes for an immunogenic protein. The cellular immune response is critically important for the control of CMV reactivation and a large proportion of the T cell repertoire in healthy CMV-positive individuals is dedicated to this virus. However, recent work suggests that chronic CMV infection is a major challenge to the immune system. Although it may cause life-threatening complications in immunosuppressed individuals, chronic CMV infection in immunocompetent individuals is usually in apparent. # CytoTrol_CytoTrol_4.fcs CytoTrol_CytoTrol_4.Cytomegalovirus (CMV) is the largest known human herpes virus. # CytoTrol_CytoTrol_3.fcs CytoTrol_CytoTrol_3.fcs C2_Tcell
# CytoTrol_CytoTrol_2.fcs CytoTrol_CytoTrol_2.fcs C2_Tcell
# CytoTrol_CytoTrol_1.fcs CytoTrol_CytoTrol_1.fcs C2_Tcell , subset = `EXPERIMENT NAME` = "C2_Tcell" gs <- flowjo_to_gatingset(ws, name = 4, execute = FALSE, additional.keys = NULL Note that the columns referred by the expression must also be explicitly specified in ‘keywords’ argument, which we will cover in the later sections.Į.g. Or an that is similar to the one passed to ‘base::subset’ function to filter a ame. gs <- flowjo_to_gatingset(ws, name = 4, execute = FALSE, additional.keys = NULL, subset = c("CytoTrol_CytoTrol_3.fcs")) Or the vector of sample (FCS) names, e.g. SampleNames(gs) # "CytoTrol_CytoTrol_1.fcs" "CytoTrol_CytoTrol_2.fcs" gs <- flowjo_to_gatingset(ws, name = 4, execute = FALSE, additional.keys = NULL, subset = 1:2) Subset argument takes numeric indies, e.g. Sometime it is useful to only select a small subset of samples to import to quickly test or review the content of gating tree instead of waiting for the entire data set to be completed, which could take longer time if the total number of samples is big. gs_pop_get_data(gs) # Error in y): gate is not parsed! Otherwise it will display the value computed from FCS file, which will be NA in this case since we didn’t load FCS files.Īpparently, it is very fast to only import xml, but data won’t be available for retrieving. Note that xml flag needs to be set in order to tell it to return the stats from xml file. # stop("'deriv' must be between 0 and 3")Īnd stats head(gs_pop_get_stats(gs, xml = TRUE)) # sample pop count Transformations gh_get_transformations(gs], channel = "B710-A") # function (x, deriv = 0) # Compensation object 'defaultCompensation': # Ellipsoid gate 'CD3+' in dimensions and SSC-AĬompensations gs_get_compensations(gs) # $CytoTrol_CytoTrol_1.fcs_119531
It is possible to only import the gating structure without reading the FCS data by setting execute flag to FALSE.