Table 2 Advantages and drawbacks of scRNA-seq of the CSF

Hypothesis-free in-depth characterization of cell populations [41]

Due to a low starting amount, transcripts can be missed during transverse transcription (“dropouts”), leading to a limited gene coverage [77]

Detection of novel disease- and cell-type-specific biomarkers

False positive and false negative DE genes can lead to false discoveries [98]

Can be combined with published CSF datasets to increase statistical power or non-CSF datasets to compare cell abundances or phenotypes between compartments, which improves the reproducibility across studies

Batch effect can be misinterpreted as novel biological findings while correction of batch effects entails the risk of removing biological variation [80, 99]

Wide range of analyses possible with a plethora of computation tools [100]

Analyses remain mostly descriptive and cannot substitute mechanistic experiments [101]

Because of limited CSF cell counts, deep-sequencing of CSF cells is affordable

Number of total available cells by limited by low CSF cell counts, thus relative cell frequencies can be biased and rare cell populations might be completely missed

Increasingly multi-dimensional data collected simultaneously (proteome, transcriptome, epigenome)

Because of limited CSF cell counts, differential expression of rare cell populations between conditions can be unreliable [102]