An age specific platelet differentiation path from hematopoietic stem cells causes thrombocytosis and exacerbated thrombosis
Here we reveal a direct differentiation pathway from hematopoietic stem cells into platelets that is unique to aging. We used single cell RNA sequencing (scRNA-seq) to analyze age specific platelet pathway at the clonal level. Overall design: Cell preparation and sorting: Cells were harvested and c-Kit-enriched from the long bones and hips of the hind legs of young and old mice as described above. Cc-Kit-enriched cells were then stained for lineage markers via a purified rat-anti-mouse antibody cocktail (CD3, CD4, CD5, CD8, CD11b, B220, Gr-1, and Ter119) on ice for 30 minutes. Cells were washed three times followed by incubation with goat-anti-rat Alexa Fluor 680 secondary on ice in the dark for 20 minutes. Cells were washed three times then resuspended in blocking solution (10% bovine calf serum (BCS), 10% normal mouse serum (Invitrogen), and 10% normal rat serum (Invitrogen) in 1X PBS (Cytiva). Cells were blocked for 10 minutes on ice in the dark. Following block, directly conjugated antibodies were directly added to the cells (CD117/cKit and CD150) and incubated on ice in the dark for 45 minutes, gently mixing the cells at the 30-minute mark. A small aliquot of cells was also used to perform a CD150 fluorescence minus one (FMO) control. Cells were washed three times, resuspended in 10% BCS in 1X PBS (wash buffer), with three drops of propidium iodide (diluted 1:5000 in wash buffer) added. Live, lineage-c-Kit+CD150- and live, lineage-c-Kit+CD150+ young and old cells were sorted on a BD FACSAria IIu (BD Biosciences) (Figure S6). The CD150- and CD150+ gates were touching to ensure a continuum of cells across CD150 expression were sorted. Cells were sorted into chilled 1X PBS (without Mg2+) containing 20% BCS and checked for sort purity. Following sorting, cells were washed and each of the four sorted populations (young CD150-, young CD150+, old CD150-, and old CD150+) were split into three aliquots for Cell Multiplexing Oligo (CMO) labeling (10X Genomics) following manufacturer's instructions, with each individual group of cells receiving a single unique CMO tag. Cells were washed three times with wash buffer and manually counted via a hemocytometer. When possible, equal numbers of cells from each of the 12 CMO labeled aliquots were combined to artificially inflate the numbers of rare CD150+ cells and to equalize numbers between young and old mice. The pooled cells were then split into two replicates for further processing. Sample sequencing and preparation: Both pooled replicates were used in parallel in the Chromium Next GEM single Cell 3' v3.1 protocol (10X Genomics) following manufacturer's directions. GEM generation and barcoding was performed immediately post-sorting, with resulting samples stored at -20ºC per manufacturer's instructions. All other steps were performed simultaneously for all samples from each of the two independent experiments. Following library construction, samples were assessed via an Agilent Bioanalyzer with a High Sensitivity chip (Agilent Technologies). Final sample libraries were sequenced on a Novaseq P100 at the University of California Davis DNA Technologies and Expression Analysis Core, targeting 50,000 read pairs per cell for the gene expression libraries and 5,000 reads per cell for the CMO libraries. Data analysis – quality control and data processing. Sequencing data was initially processed via the 10X Genomics Cell Ranger Cloud pipeline, using the mouse genome (mm10) modified to allow for detection of reporter transgene RNA expression. For the downstream analysis, cells with expression of fewer than 200 genes were excluded, and genes detected in fewer than three cells were removed from subsequent analysis steps. Mitochondrial and ribosomal gene transcripts, as well as Malat1 transcripts, were removed to reduce technical bias. This resulted in a dataset of 22,790 cells and 22,870 genes. Additionally, 13 cells with absent Kit RNA were filtered out. Doublets identified by DoubletDetection 2.4.0 (p_thresh=1e-16, voter_thresh=0.5) removed 157 cells in total (Gayoso et al. 2018).  The UMI counts for each cell were normalized by the total counts across all genes (target_sum=1e4), and log-transformed with an added pseudo count of 1.