A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry

Precisely controlled development of the somatosensory system is essential for detecting pain, itch, temperature, mechanical touch and body position. To investigate the protein-level changes that occur during somatosensory development, we performed single-cell mass cytometry on dorsal root ganglia from C57/BL6 mice of both sexes, with litter replicates collected daily from embryonic day 11.5 to postnatal day 4. Measuring nearly 3 million cells, we quantified 30 molecularly distinct somatosensory glial and 41 distinct neuronal states across all timepoints. Analysis of differentiation trajectories revealed rare cells that co-express two or more Trk receptors and over-express stem cell markers, suggesting that these neurotrophic factor receptors play a role in cell fate specification. Comparison to previous RNA-based studies identified substantial differences between many protein-mRNA pairs, demonstrating the importance of protein-level measurements to identify functional cell states. Overall, this study demonstrates that mass cytometry is a high-throughput, scalable platform to rapidly phenotype somatosensory tissues.

FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.

AN UPDATED DEBARCODING TOOL FOR MASS CYTOMETRY WITH CELL TYPE-SPECIFIC AND CELL SAMPLE-SPECIFIC STRINGENCY ADJUSTMENT

Pooled sample analysis by mass cytometry barcoding carries many advantages: reduced antibody consumption, increased sample throughput, removal of cell doublets, reduction of cross-contamination by sample carryover, and the elimination of tube-to-tube-variability in antibody staining. A single-cell debarcoding algorithm was previously developed to improve the accuracy and yield of sample deconvolution, but this method was limited to using fixed parameters for debarcoding stringency filtering, which could introduce cell-specific or sample-specific bias to cell yield in scenarios where barcode staining intensity and variance are not uniform across the pooled samples. To address this issue, we have updated the algorithm to output debarcoding parameters for every cell in the sample-assigned FCS files, which allows for visualization and analysis of these parameters via flow cytometry analysis software. This strategy can be used to detect cell type-specific and sample-specific effects on the underlying cell data that arise during the debarcoding process. An additional benefit to this strategy is the decoupling of barcode stringency filtering from the debarcoding and sample assignment process. This is accomplished by removing the stringency filters during sample assignment, and then filtering after the fact with 1- and 2-dimensional gating on the debarcoding parameters which are output with the FCS files. These data exploration strategies serve as an important quality check for barcoded mass cytometry datasets, and allow cell type and sample-specific stringency adjustment that can remove bias in cell yield introduced during the debarcoding process.