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We developed a microfluidics-based model to quantify cell-level processes modulating the pathophysiology of sickle cell disease (SCD). This in vitro model enabled quantitative investigations of the kinetics of cell sickling, unsickling, and cell rheology. We created short-term and long-term hypoxic conditions to simulate normal and retarded transit scenarios in microvasculature. Using blood samples from 25 SCD patients with sickle hemoglobin (HbS) levels varying from 64 to 90.1%, we investigated how cell biophysical alterations during blood flow correlated with hematological parameters, HbS level, and hydroxyurea (HU) therapy. From these measurements, we identified two severe cases of SCD that were also independently validated as severe from a genotype-based disease severity classification. These results point to the potential of this method as a diagnostic indicator of disease severity. In addition, we investigated the role of cell density in the kinetics of cell sickling. We observed an effect of HU therapy mainly in relatively dense cell populations, and that the sickled fraction increased with cell density. These results lend support to the possibility that the microfluidic platform developed here offers a unique and quantitative approach to assess the kinetic, rheological, and hematological factors involved in vasoocclusive events associated with SCD and to develop alternative diagnostic tools for disease severity to supplement other methods. Such insights may also lead to a better understanding of the pathogenic basis and mechanism of drug response in SCD., National Institutes of Health (U.S.) (R01HL094270), National Institutes of Health (U.S.) (U01HL114476)

We have used a microfluidic mass sensor to measure the density of single living cells. By weighing each cell in two fluids of different densities, our technique measures the single-cell mass, volume, and density of approximately 500 cells per hour with a density precision of 0.001 g mL-1. We observe that the intrinsic cell-to-cell variation in density is nearly 100-fold smaller than the mass or volume variation. As a result, we can measure changes in cell density indicative of cellular processes that would be otherwise undetectable by mass or volume measurements. Here, we demonstrate this with four examples: identifying Plasmodium falciparum malaria-infected erythrocytes in a culture, distinguishing transfused blood cells from a patient’s own blood, identifying irreversibly sickled cells in a sickle cell patient, and identifying leukemia cells in the early stages of responding to a drug treatment. These demonstrations suggest that the ability to measure single-cell density will provide valuable insights into cell state for a wide range of biological processes., EUREKA (Exceptional, Unconventional Research Enabling Knowledge Acceleration (R01GM085457)), National Institutes of Health (U.S.) (NIH Cell Decision Process Center Grant (P50GM68762)), United States. Army Research Office (Institute for Collaborative Biotechnologies Grant (W911NF-09-D-0001)), Massachusetts Institute of Technology (Whitaker Health Sciences Graduate Fellowship), National Institutes of Health (U.S.) (Grant R01HL094270), National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant DK083242)

We have used a microfluidic mass sensor to measure the density of single living cells. By weighing each cell in two fluids of different densities, our technique measures the single-cell mass, volume, and density of approximately 500 cells per hour with a density precision of 0.001 g mL-1. We observe that the intrinsic cell-to-cell variation in density is nearly 100-fold smaller than the mass or volume variation. As a result, we can measure changes in cell density indicative of cellular processes that would be otherwise undetectable by mass or volume measurements. Here, we demonstrate this with four examples: identifying Plasmodium falciparum malaria-infected erythrocytes in a culture, distinguishing transfused blood cells from a patient’s own blood, identifying irreversibly sickled cells in a sickle cell patient, and identifying leukemia cells in the early stages of responding to a drug treatment. These demonstrations suggest that the ability to measure single-cell density will provide valuable insights into cell state for a wide range of biological processes., EUREKA (Exceptional, Unconventional Research Enabling Knowledge Acceleration (R01GM085457)), National Institutes of Health (U.S.) (NIH Cell Decision Process Center Grant (P50GM68762)), United States. Army Research Office (Institute for Collaborative Biotechnologies Grant (W911NF-09-D-0001)), Massachusetts Institute of Technology (Whitaker Health Sciences Graduate Fellowship), National Institutes of Health (U.S.) (Grant R01HL094270), National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant DK083242)