Discovery	FACS panels are focused on known cell types. Unbiased approaches such as CITE-seq with 50 antibodies yield 1225 dot plots, many of which have never been seen before. This will lead to the discovery of new cell types in blood and tissues of humans and model animals in health and disease. The surface phenotype is superior to transcriptomes for cell identification, because it takes advantage of knowledge gained in 30 years of flow cytometry.
Complexity	Classical flow cytometry allows for detection of 16 markers. New analyzers promise up to 50 markers (Cytek Aurora, BD Symphony). Mass cytometry resolves up to 40 markers. Due to uniquely barcoded antibodies, CITE-seq is virtually unlimited. 100-plex panels are on the horizon.
Uncovering true heterogeneity	Machine learning algorithms perform dimensionality reduction (UMAP, tSNE) and group cells into clusters of similar surface antibody expression (Louvain). In a second analysis step, the single cell transcriptomes refine the clusters.
TCR, BCR	T and B cell receptor sequences can be assembled, for example using the 5’ solution by 10x Genomics, and combined with transcriptomes and surface phenotype.
Leveraging transcriptomes	Pathway analysis allows to determine the functionality of a given cell cluster, including activation, proliferation and apoptosis status.
Developmental cues	Single cell transcriptomes can be subjected to algorithms such as Monocle and RNAvelocity, which render data as pseudotime plots based on the expression status of a cell. This allows to infer developmental trajectories and lineage branching of cells in a complex environment.
Limitations	All scRNA-seq approaches require enzymatic and mechanical tissue dissociation, which induces artifacts. CITE-seq or AB-seq are not compatible with intracellular staining. The assays for CITE-seq or AB-seq are more elaborate and require a higher technical skill compared to sample preparation for flow cytometry. The workflooxw requires accessibility to multiple instruments including a FACS sorter, single cell platforms such as the 10x Chromium Controller and a sequencer. The biggest bottleneck today is the bioinformatic analysis of the generated data.