The primary cilium is able to maintain a specific protein composition, which is critical for its function as a signaling organelle. Here we introduce a system to synchronize biosynthetic trafficking of ciliary proteins that is based on conditional aggregation domains (CADs). This approach enables to create a wave of ciliary proteins that are transported together, which opens novel avenues for visualizing and studying ciliary import mechanisms. By using somatostatin receptor 3 (SSTR3) as model protein we studied intracellular transport and ciliary import with high temporal and spatial resolution in epithelial Madin-Darby canine kidney (MDCK) cells. This yielded the interesting discovery that SSTR3, besides being transported to the primary cilium, is also targeted to the basolateral plasma membrane. In addition, we found a similar behavior for another ciliary protein, nephrocystin-3 (NPHP3), thus suggesting a potential correlation between ciliary and basolateral trafficking. Furthermore, our CAD-based system allowed assembling a large dataset in which apical and basolateral surface SSTR3 signals could be compared to ciliary SSTR3 signals on a single cell level. This enabled to generate novel complementary evidence for the previously proposed lateral import mechanism of SSTR3 into the cilium along the plasma membrane.

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Single-cell RNA sequencing (scRNA-seq) can unmask transcriptional heterogeneity facilitating the detection of rare subpopulations at unprecedented resolution. In response to challenges related to coverage and quantity of transcriptome analysis, the lack of unbiased and quantitative validation methods hampers further improvements. Digital PCR (dPCR) represents such a method as the inherent partitioning could enhance detection by increasing the effective concentration of nucleic acids. Thus, we have developed a novel, integrated workflow combining down-scaled, single-cell Smart-seq2 and absolute quantitative, single-cell digital PCR. Our workflow reduces biological and technical variability by analyzing single cells from the same population using identical steps for liquid and single-cell handling. Using two breast cancer cell lines, MCF7 and BT-474, we validated the performance of our down-scaled Smart-seq2 by comparative clustering with published data and our scRT-ddPCR by comparison of absolute gene mRNA counts to bulk methods. We observed significant differences in signal distributions of low-abundant ErbB2 in MCF7 between scRNA-seq and scRT-ddPCR. The differences are mirrored in the calculated fold changes. We assume that low-abundant transcripts suffer from dropouts in scRNA-seq while high abundant transcripts (such as ACTB or ErbB2 in BT-474 cells) suffer from minimal dropouts in the down-scaled Smart-seq2. In scRT-ddPCR however, the inherent partitioning of the reaction volume increases the effective concentration of mRNAs and thus improves sensitivity. As a conclusion, our workflow combines transcriptome-wide gene expression profiling by scRNA-seq with the precise and reliable determination of absolute gene mRNA per cell counts and fold changes by scRT-ddPCR. This can correct biased conclusions made solely on the basis of scRNA-seq. We think this workflow is a valuable addition to the single-cell transcriptomic research toolbox and could even become a new standard in fold change validation because of its reliability, ease of use and increased sensitivity.