Summary Stress erythropoiesis and chronic inflammation in subjects with sickle cell disease (SCD) may have an impact on the bone marrow (BM) haematopoietic stem and progenitor cell (HSPC) quality and yield necessary for effective autologous, ex vivo HSPC gene therapy. BM from 19 subjects with SCD and five volunteers without SCD (non‐SCD) was collected in different anticoagulants and processed immediately (day 0) or the following day (day 1). Inflammatory, contamination and aggregation markers within the mononuclear layer, and CD34, CD45 and Glycophorin‐A (GPA) expression on HSPCs after CD34+ selection were analysed by conventional and imaging flow cytometry. Compared to non‐SCD BM, multiple markers of inflammation, contamination (red cells, P < 0·01; platelets, P < 0·01) and aggregates (platelet/granulocytes, P < 0·01; mononuclear/red cells, P < 0·01) were higher in SCD BM. Total CD34+ cell count was lower in SCD BM (P < 0·05), however CD34+ count was higher in SCD BM when collected in acid citrate dextrose‐A (ACDA) versus heparin (P < 0·05). Greater than 50% of CD34+ HSPCs from SCD BM are CD34dim due to higher erythroid lineage expression (P < 0·01) as single cell CD34+CD45+GPA+ (P < 0·01) and CD34+CD45−GPA+ (P < 0·01) HSPCs. SCD BM is characterized by increased inflammation, aggregation and contamination contributing to significant differences in HSPC quality and yield compared to non‐SCD BM.

Sickle cell anemia (SCA) is associated with a hypercoagulable state. Increased platelet activation is reported in SCA and SCA platelets may present augmented adhesion to the vascular endothelium, potentially contributing to the vaso-occlusive process. We sought to observe the effects of platelets (PLTs) from healthy control (CON) individuals and SCA individuals on endothelial activation, in vitro. Human umbilical vein endothelial cells (HUVEC) were cultured, in the presence, or not, of washed PLTs from CON or steady-state SCA individuals. Supernatants were reserved for cytokine quantification, and endothelial adhesion molecules (EAM) were analyzed by flow cytometry; gene expressions of ICAM1 and genes of the NF-κB pathway were analyzed by qPCR. SCA PLTs were found to be more inflammatory, displaying increased adhesive properties, an increased production of IL-1β and a tendency towards elevated expressions of P-selectin and activated αIIbβ3. Following culture in the presence of SCA PLTs, HUVEC presented significant augmentations in the expressions of the EAM, ICAM-1 and E-selectin, as well as increased IL-8 production and increased ICAM1 and NFKB1 (encodes p50 subunit of NF-κB) gene expressions. Interestingly, transwell inserts abolished the effects of SCA PLTs on EAM expression. Furthermore, an inhibitor of the NF-κB pathway, BAY 11-7082, also prevented the induction of EAM expression on the HUVEC surface by SCA PLTs. In conclusion, we find further evidence to indicate that platelets circulate in an activated state in sickle cell disease and are capable of stimulating endothelial cell activation. This effect appears to be mediated by direct contact, or even adhesion, between the platelets and endothelial cells and via NFκB-dependent signaling. As such, activated platelets in SCD may contribute to endothelial activation and, therefore, to the vaso-occlusive process. Results provide further evidence to support the use of anti-platelet approaches in association with other therapies for SCD.

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Data analysis in imaging flow cytometry incorporates elements of flow cytometry together with other aspects of morphological analysis of images. A crucial early step in this analysis is the creation of a mask to distinguish the portion of the image upon which further examination of specified features can be performed. Default masks are provided by the manufacturer of the imaging flow cytometer but additional custom masks can be created by the individual user for specific applications. Flawed or inaccurate masks can have a substantial negative impact on the overall analysis of a sample, thus great care must be taken to ensure the accuracy of masks. Here we discuss various types of masks and cite examples of their use. Furthermore we provide our insight for how to approach selecting and assessing the optimal mask for a specific analysis.