2014
DOI: 10.1182/blood-2014-03-559054
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Imaging flow cytometry documents incomplete resistance of human sickle F-cells to ex vivo hypoxia-induced sickling

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Cited by 6 publications
(4 citation statements)
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“…These results also imply that when investigating the influence of HbF, the average concentration of HbF in a cell population is less important than the HbF content in individual RBCs (53). This interpretation is supported by an ex vivo study showing incomplete resistance of F cells in hypoxia-induced sickling (56).…”
Section: Discussionmentioning
confidence: 63%
“…These results also imply that when investigating the influence of HbF, the average concentration of HbF in a cell population is less important than the HbF content in individual RBCs (53). This interpretation is supported by an ex vivo study showing incomplete resistance of F cells in hypoxia-induced sickling (56).…”
Section: Discussionmentioning
confidence: 63%
“…This method could be applied to other hemoglobins with appropriate antibodies. Moreover, combining quantitative measurements of different hemoglobins and the total hemoglobin per RBC, would inform about their relative amount and concentration, which is of interest for an anti‐polymerization effect, using the corpuscular Hb concentration (MCHC) per RBC 21,28 . However, the latter requires an individual volume measurement which is difficult to obtain when the shape is not regular as in SCD.…”
Section: Discussionmentioning
confidence: 99%
“…further evaluated the concentration of intracellular HbF on morphology of individual sickle RBCs. 57 Deoxygenated sickle RBCs are first fluorescently labeled against HbF and then imaged using IFC which determines the HbF expression and morphology of the cells. 57 Automated classification of RBC morphologies can also be accomplished by standardizing acquisition of high-resolution microscopic images of individual RBCs using microfluidic devices, and then automatically classifying RBC shape using standard decision-tree-based algorithms or novel approaches based on deep learning (Figure 3).…”
Section: Rbc Morphologymentioning
confidence: 99%
“…57 Deoxygenated sickle RBCs are first fluorescently labeled against HbF and then imaged using IFC which determines the HbF expression and morphology of the cells. 57 Automated classification of RBC morphologies can also be accomplished by standardizing acquisition of high-resolution microscopic images of individual RBCs using microfluidic devices, and then automatically classifying RBC shape using standard decision-tree-based algorithms or novel approaches based on deep learning (Figure 3). 40,58 These image analysis techniques can also be applied to a variety of traditional protocols (such as basic peripheral blood smears and sickling assays) as well as emerging microfluidic technologies that investigate RBC sickling behavior under hypoxia.…”
Section: Rbc Morphologymentioning
confidence: 99%