Variable importance accounts for the increase in out-of-bag cross-validated prediction error. It would be possible but not meaningful to account for the change of prediction error by one sample only. As one sample only can be correctly or wrongly predicted, such a term would be very unstable and crude.
You could check out 'local variable importance', 'partial dependence plots' or 'feature contributions'. Here's an example from my package forestFloor using feature contributions. Each plot shows the change of predicted class probability as function each variable. For the iris data set, there no strong variable interactions. Therefore, the model structure can be boiled down to a 2D visualization. The R-sqaured terms quantifies how much the model structure deviates from this main effect only interpretation/visualization.
X = iris[,!names(iris) %in% "Species"]
Y = iris[,"Species"]
rf = randomForest(X,Y,
keep.forest=TRUE, #mandatory for classification
replace=FALSE, #if TRUE use trimTrees::cinbag, not randomForest
keep.inbag=TRUE, #mandatory always for forestFloor
sampsize =15 ) #optional:smaller trees smoother model structure
ff = forestFloor(rf.fit = rf, # mandatory
X = X, # mandatory
calc_np = "sad monkey", # this input takes no effect for classification
binary_reg = FALSE) # can change two class classification to regression
# Thus cannot be TRUE for IRIS (three class)