Less Deformable Erythrocyte Subpopulations Biomechanically Induce Endothelial Inflammation in Sickle Cell Disease

Sickle cell disease (SCD) is canonically characterized by reduced red blood cell (RBC) deformability leading to microvascular obstruction and inflammation. While the biophysical properties of sickle RBCs are known to influence SCD vasculopathy, the contribution of poor RBC deformability to endothelial dysfunction has yet to be fully explored. Leveraging interrelated in vitro and in silico approaches, we introduce a new paradigm of SCD vasculopathy in which poorly deformable sickle RBCs directly cause endothelial dysfunction via mechanotransduction, where endothelial cells sense and pathophysiologically respond to aberrant physical forces independently of microvascular obstruction, adhesion, or hemolysis. We demonstrate that perfusion of sickle RBCs or pharmacologically-dehydrated healthy RBCs into small venule-sized “endothelialized” microfluidics leads to pathologic physical interactions with endothelial cells that directly induce inflammatory pathways. Using a combination of computational simulations and large venule-sized endothelialized microfluidics, we observed that perfusion of heterogeneous sickle RBC subpopulations of varying deformability, as well as suspensions of dehydrated normal RBCs admixed with normal RBCs leads to aberrant margination of the less-deformable RBC subpopulations towards the vessel walls, causing localized, increased shear stress. Increased wall stress is dependent on the degree of subpopulation heterogeneity and oxygen tension and leads to inflammatory endothelial gene expression via mechanotransductive pathways. Our multifaceted approach demonstrates that the presence of sickle RBCs with reduced deformability leads directly to pathological physical (i.e., direct collisions and/or compressive forces) and shear-mediated interactions with endothelial cells and induces an inflammatory response, thereby elucidating the ubiquity of vascular dysfunction in SCD. Overall design: Single-cell RNA sequencing (scRNA-seq) was performed on human umbilical vein endothelial cells (HUVECs) collected after exposure to healthy red blood cells (hRBCs) or a binary suspension of 5% pharmacologically-dehydrated RBCs (pdRBCs) in hRBCs, using a four-channel 50 µm microfluidic device. To mimic the decreased deformability of sickle RBCs, hRBCs were pharmacologically-dehydrated with nystatin and high sucrose washes according to established practices to create RBCs with an increased mean corpuscular hemoglobin concentration of 40-42 g/dL. Following endothelialization of the microfluidic channels, the hRBC or 5% pdRBCs were perfused according to our established protocol. After endothelial cells were collected from the microfluidic devices and resuspended in media, the suspension was passed through 70 and 40 µm cell strainers and centrifuged at 500xg at 4°C for 10 minutes. For RBC lysis, ACK buffer was added. For immediate single-cell capture, the cells were pelleted at 400xg at 4°C for 5 minutes and resuspended in PBS containing 1.0% BSA to get a concentration of 500 cells/µl. For single-cell RNA sequencing (scRNA-seq), barcoded gel bead-in-emulsions (GEMs) were generated from single-cell suspensions with gel beads and reverse transcription (RT) mix on the Chromium Controller, following the 10x Genomics workflow. Following RT and cDNA amplification, the Chromium Next GEM 5'reagent kit v2 (10x Genomics) was used to generate 5' gene expression libraries. Gene expression libraries were prepared using the Chromium Next GEM 5'reagent kits v2 (10x Genomics). GEMs were generated and barcoded by loading single-cell suspensions along with gel beads and reverse transcription master mix onto Chromium Next GEM Chip K and running on the 10x Genomics chromium controller. Following reverse transcription, the cDNA was amplified and used to generate 5' gene expression libraries. The cDNA and gene expression libraries were quantified using a Qubit 3.0 fluorometer, and quality was assessed using HS DNA chips and 2100 Bioanalyzer (Agilent Technologies). Sequencing was performed using massively parallel sequencing on the Illumina Novaseq S4 platform, targeting ~50,000 reads/cell to capture the expression of ~1000–2000 transcripts/cell.