Author response: Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of diluted whole blood, without labeling, enrichment or separation, at rates of 1000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in blood and adds a functional dimension to conventional blood analysis. https://doi.org/10.7554/eLife.29213.001 eLife digest When you are sick and go to the doctor, it is often not obvious what exactly is wrong — what is causing fever, nausea, shortness of breath or other symptoms. It is important to find this out quickly so that the right action can be taken. One of the first steps is to obtain a blood sample and to count how many of the different blood cells are present in it. This is called a complete blood count, and the information it provides has turned out to be surprisingly useful. A large number of certain white blood cells, for example, can show that the body is fighting an infection. But there might be several reasons why the number of white blood cells has increased, so this information alone is often not enough for a specific diagnosis. There are many hundreds of possible tests that can supplement the results of a complete blood count. These might identify bacteria or measure the concentrations of certain molecules in the blood, for example. But which test will give the important clue that reveals the source of the illness? This can be difficult to predict. Although each test helps to narrow down the final diagnosis they become increasingly expensive and time-consuming to perform, and rapid action is often important when treating a disease. Can we get more decisive information from the initial blood test by measuring other properties of the blood cells? Toepfner et al. now show that this is possible using a technique called real-time deformability cytometry. This method forces the blood cells in a small drop of blood to flow extremely rapidly through a narrow microfluidic channel while they are imaged by a fast camera. A computer algorithm can then analyze the size and stiffness of the blood cells in real-time. Toepfner et al. show that this approach can detect characteristic changes that affect blood cells as a result of malaria, spherocytosis, bacterial and viral infections, and leukemia. Furthermore, many thousands of blood cells can be measured in a few minutes — fast enough to be suitable as a diagnostic test. These proof-of-concept findings can now be used to develop actual diagnostic tests for a wide range of blood-related diseases. The approach could also be used to test which of several drugs is working to treat a certain medical condition, and to monitor whether the treatment is progressing as planned. https://doi.org/10.7554/eLife.29213.002 Introduction Blood is responsible for the distribution of oxygen and nutrients, and centrally involved in the immune response. Consequently, its analysis yields crucial information about the health status of patients. The complete blood count, the analysis of presence and frequency of all major blood cells, constitutes a basic, routine measure in clinical practice. It is often accompanied by analysis of blood biochemistry and molecular markers reflecting the current focus on molecular considerations in biology and biomedicine. An orthogonal approach could be seen in the study of the overall rheological properties of blood. It is evident that the flow of blood throughout the body will be determined by its physical properties in the vasculature, and their alterations could cause or reflect pathological conditions (Lichtman, 1973; Baskurt and Meiselman, 2003; Popel and Johnson, 2005). In this context, blood is a poly-disperse suspension of colloids with different deformability and the flow properties of such non-Newtonian fluids have been the center of study in hydrodynamics and colloidal physics (Lan and Khismatullin, 2012). Probably due to the dominant importance of erythrocytes, at the expense of sensitivity to leukocyte properties, whole blood rheology has not resulted in wide-spread diagnostic application. Focusing on the physical properties of individual blood cells has suggested a third possibility to glean maximum diagnostic information from blood. Various cell mechanics measurement techniques, such as atomic force microscopy (Worthen et al., 1989; Rosenbluth et al., 2006; Lam et al., 2008), micropipette aspiration (Lichtman, 1973; Lichtman, 1970; Dombret et al., 1995; Ravetto et al., 2014) or optical traps (Lautenschläger et al., 2009; Ekpenyong et al., 2012; Paul et al., 2013), have been used to show that leukocyte activation, leukemia, and malaria infection, amongst many other physiological and pathological changes, lead to readily quantifiable mechanical alterations in the major blood cells (Worthen et al., 1989; Lautenschläger et al., 2009; Schmid-Schönbein et al., 1973; Suresh et al., 2005; Rosenbluth et al., 2008; Bow et al., 2011; Gossett et al., 2012). These proof-of-concept studies have so far been done on few tens of specifically isolated cells. This line of research has not progressed towards clinical application for lack of an appropriate measurement technique that can assess single-cell properties of sufficient number directly in blood. This report aims to close this gap by presenting a novel approach for high-throughput single-cell morpho-rheological (MORE) characterization of all major blood cells in continuous flow. Mimicking capillary flow, we analyze human blood without any labeling or separation at rates of 1000 cells/sec. We show that we can sensitively detect morphological and rheological changes of erythrocytes in spherocytosis and malaria infection, of leukocytes in viral and bacterial infection, and of malignant transformed cells in myeloid and lymphatic leukemias. Readily available quantitative morphological parameters such as cell shape, size, aggregation, and brightness, as well as rheological information of each blood cell type with excellent statistics might not only inform further investigation of blood as a complex fluid. It also connects many previous reports of mechanical changes of specifically isolated cells to a measurement now done directly in blood. As such, it adds a new functional dimension to conventional blood analysis — a MORE complete blood count — and, thus, opens the door to a new era of exploration in investigating and diagnosing hematological and systemic disorders. Results Establishment of morpho-rheological analysis In order to establish the normal MORE phenotype of cells found in blood, we obtained venous, citrate-anticoagulated blood of healthy donors, of which 50 µl was diluted in 950 µl of measurement buffer with a controlled elevated viscosity, but without any additional labeling, sorting, or enrichment. The cell suspension was then pumped through a micro-channel not unlike micro-capillaries in the blood vasculature (Figure 1A). Brightfield images of the cells, deformed by hydrodynamic shear stresses in the channel (Mietke et al., 2015), were obtained continuously by RT-DC (Otto et al., 2015) (see Materials and methods; Video 1). These images revealed distinct differences in overall morphology, brightness, and amount of deformation between all major cell types found in blood (Figure 1B). RT-DC further enabled the continuous, real-time quantification of the cross-sectional area and of the deformed shape (see detailed description in Materials and methods and Figure 1—figure supplement 1) of an, in principle, unlimited number of cells at measurement rates of 100–1000 cells/sec (Figure 1C). For each cell detected and analyzed, an image was saved and the average pixel brightness within the cell determined (Figure 1D, Figure 1—figure supplement 1). This single-cell MORE analysis of blood revealed distinct and well-separated cell populations in the space spanned by the three parameters (Video 2). Notably, size and brightness alone — parameters not unlike those accessible by light scattering analysis in standard flow cytometers — were sufficient for the identification of the cell types (Figure 1D), so that deformation, as an additional and independent parameter, was available for assessing their functional changes. The identity of the individual cell populations by size and brightness was established by magnetic cell sorting, controlled by fluorescence immunophenotyping, and subsequent MORE analysis (Figure 1—figure supplement 2). A key feature is the very clear separation of the abundant erythrocytes (red blood cells; RBCs) from other cells as a result of their much greater deformation and lower brightness. This feature gives access to leukocyte properties directly in diluted whole blood, without the potentially detrimental effects of hemolysis (see Figure 1—figure supplement 3) or other separation steps, which are required for analysis with cell mechanics techniques with lower specificity and throughput, or non-continuous measurement. This aspect contributes to the well-established field of hemorheology the possibility to interrogate mechanical properties of all individual blood cells and to specifically investigate their contribution to the overall blood rheological properties. Figure 1 with 6 supplements see all Download asset Open asset Single-cell, morpho-rheological phenotyping of blood. (A) Analysis of whole, diluted blood. Hydrodynamic shear forces (red arrows) induce deformation of cells passing through a microfluidic channel (20 × 20 µm2) at speeds of more than 30 cm/s (blue arrows). (B) Representative images of blood cell types acquired. Scale bar is 10 µm. Images are analyzed for cell size as well as (C) cell deformation and (D) average cell brightness. Each dot represents one of N measurement events. (E) Normal range of deformation and size of cell populations from healthy donors. Each diamond represents the median of one donor; transparent ellipses indicate 95% confidence areas. (F) Comparison of MORE cell counts with conventional blood count. https://doi.org/10.7554/eLife.29213.003 Figure 1—source data 1 Source data for Figure 1. https://doi.org/10.7554/eLife.29213.010 Download elife-29213-fig1-data1-v2.xlsx Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg RT-DC in action. Video of the microfluidic channel system during RT-DC measurement of diluted whole blood. The cell suspension flows from left to right through the channel. Cells enter on the left and are focused by sheath flow from the top and bottom of the frame towards the narrow RT-DC measurement channel of 300 µm length and 20 µm width and height in the right half of the image. RT-DC measurements are carried out on the cells that travel through the last third of the length of the measurement channel. https://doi.org/10.7554/eLife.29213.011 Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg 3D visualization of the separation of leukocyte populations. Rotating angle view in the space of deformation, cell size and cell brightness. Cell identification in order of appearance by coloring: lymphocytes (blue), neutrophil granulocytes (black), eosinophil granulocytes (cyan), monocytes (orange), basophil granulocytes (green). https://doi.org/10.7554/eLife.29213.012 In extensive tests of the variability of this approach, MORE phenotyping yielded identical results in repeated measurements of blood from the same donor, with sodium citrate added as an anti-coagulant and for different storage times (Figure 1—figure supplement 4), between different donors of both sexes (Figure 1—figure supplement 5), and blood samples taken at different times during the day (Figure 1—figure supplement 6). This robustness served to establish a norm for the different cell types (Figure 1E). MORE analysis provided the identity and frequency of all major white blood cells as with a conventional differential blood count (Figure 1F; Supplementary file 1) — obtained from a single drop of blood, with minimal preparation, and within 15 min. Going beyond this current gold-standard of routine blood cell analysis, and importantly also beyond all other single-blood-cell mechanical analysis studies to date, MORE phenotyping allowed the sensitive characterization of pathophysiological changes of individual cells directly in merely diluted whole blood. In the following, we exemplarily demonstrate, in turn for each of the blood cell types, the new possibilities of gaining MORE information from an initial blood test as a time-critical step in generating specific hypotheses and steering further investigation enabled by this approach. Detection of morpho-rheological changes in erythrocytes Spherocytosis is a prototypical hereditary disease in humans in which genetic changes (here ankyrin and spectrin mutations) cause abnormal shape and mechanical properties of erythrocytes. Current diagnosis is based on the detection of abnormal cell shapes in a blood smear, followed up by assessment of the osmotic fragility quantified by Acidified Glycerol Lysis Time (AGLT) or by osmotic gradient ektacytometry. These manual assays take time and do not lend themselves to quick, initial screening. MORE analysis of the blood of patients with spherocytosis directly revealed significantly less deformed and smaller erythrocytes than normal (Figure 2A–C) as the functional correlate of the cytoskeletal mutation. The differences are so clear (Figure 2—figure supplement 1) that this analysis can serve as a fast primary and cheap screening test for spherocytosis. Detection of such RBC changes would then warrant confirmation by more specific analysis using flow-cytometric detection of Eosin-5-Maleimide staining (EMA test) or the direct detection of the mutation by PCR, which require specific preparation, are more expensive, and thus benefit from a strong and clear initial hypothesis. Figure 2 with 2 supplements see all Download asset Open asset Detection of RBC pathologies — spherocytosis and malaria. Exemplary density plots of RBC size vs. deformation in samples from (A) healthy donor and (B) patient with spherocytosis. (C) Relative median RBC deformation and size in patients with spherocytosis (orange, n = 4 patients) compared to controls (black, n = 21 donors as in Figure 1E with 68% and 95% confidence ellipses). Density plots of size vs. deformation of (D) control RBCs and (E) RBCs exposed to P.f. (blue), both after 12 hr incubation. Insets show cells with and without image feature (white inclusion), positively indicating actual parasite infection. Infected RBCs are indicated by red dots. Scale bars, 10 µm. (F) Evolution of RBC deformation over 46 hr time course of control (black), P.f. exposed (blue) and P.f. infected RBCs (red); open squares and diamonds, mean ±SD, n = 2; filled squares, individual medians, **p<0.01, ***p<0.001. (G) Reduced deformation of 2BP-treated RBCs compared to PA- and non-treated controls (mean ±SD of population medians, n = 4 donors, *p<0.05). (H) Reduced parasitemia in 2BP- compared to PA- and non-treated controls at 2 and 4 days post infection. Error bars: SD binomial, *p<0.0125. https://doi.org/10.7554/eLife.29213.013 Figure 2—source data 1 Source data for Figure 2. https://doi.org/10.7554/eLife.29213.016 Download elife-29213-fig2-data1-v2.xlsx A change in RBC deformability has also been implicated in malaria pathogenesis, since single cells infected by parasites have been reported to be stiffer (Bow et al., 2011). This insight has not progressed towards clinical application and the gold standard in malaria diagnosis is still a manual thick blood smear analysis. To evaluate whether MORE analysis could provide a sensitive, automated alternative, we analyzed populations of RBCs exposed in vitro to Plasmodium falciparum (P.f.) with a parasitemia (percentage of actually infected cells) of 7–8% at time points over the 2 day parasite life cycle. We found a clear, significant, and increasing reduction in the deformation of the entire exposed RBC population detectable after 4 hr (Figure 2D–F; Figure 2—figure supplement 2). Inspection of the individual cell images revealed the appearance of characteristic features likely associated with the maturation of parasites inside a subset of RBCs (Figure 2D,E insets; Figure 2—figure supplement 2). These features permitted the direct identification of positively infected cells, whose relative frequency peaked at 36 hr (Figure 2—figure supplement 2). The separate assessment of overtly infected cells showed an even greater reduction in deformation than observed in the entire exposed population (Figure 2F; Figure 2—figure supplement 2), which — extrapolating our in vitro results to the situation in vivo — relates to the possibility of clearance of stiff, infected cells from the circulation by the spleen (Cranston et al., 1984; Shelby et al., 2003). However, this small fraction of stiffer cells alone cannot account for the reduced deformation of the whole population, so that a bystander stiffening of exposed but non-infected cells seems involved (Paul et al., 2013). Reduced membrane-cytoskeleton interactions have previously been correlated with elliptocytic RBCs and resistance to P.f. infection (Chishti et al., 1996). The characteristic biconcave morphology of RBCs can be chemically altered by the use of 2-bromo-palmitate (2 BP), an efficient inhibitor of palmitoyl acyltransferases (Biernatowska et al., 2013). Here, 2 BP-treated RBC samples showed changes in deformation (Figure 2G) with a concurrent reduction in P.f. infectivity (Figure 2H), compared to buffer control or RBCs treated with palmitic acid (PA). PA is an analogue of 2 BP that does not inhibit palmitoylation (Biernatowska et al., 2013). Since both, 2 BP and PA readily accumulate in the membranes, but only 2 BP causes a reduction in infectivity of P.f., we suggest that palmitoylation of RBC proteins is important for RBC morphology and infectivity of P.f. While a previous report had found no change in infectability of RBCs treated with 2 BP (Jones et al., 2012), the difference could from the different RBC involved in by the different parasite vs. which in turn are by MORE analysis has the not only to and up malaria but also to provide additional quantitative information research on the of the disease et al., Detection of morpho-rheological changes in leukocytes While RBC mechanics has been used for clinical diagnostics using and for over et al., 1973; et al., 1984; et al., leukocyte mechanics has not been for diagnostic This is likely due to their stiffness compared to RBCs and a lack of techniques of in suspension — their physiological techniques with sufficient throughput, the for specifically the cells of which the of cell change (see Figure 1—figure supplement not In this the mechanical phenotyping of diagnostic changes of leukocytes directly in diluted whole blood is the most application area of MORE analysis. For example, there have been proof-of-concept studies on the mechanical changes associated with of isolated a in line with the that is a of neutrophil (Worthen et al., 1989; Rosenbluth et al., MORE analysis of the in vitro neutrophil in blood with the bacterial that were less deformed and smaller within the first 15 post the subsequent showed a to more deformed and cells (Figure Figure supplement 1). These by themselves do not a about a change in cell since a smaller size also to less on the cells in the and less deformation (Mietke et al., et al., we also the of the cells, which from = 12 to = 20 (mean n = during the first 15 and then to but lower than = = = mechanical of has not been reported as the lower measurement of previous techniques yielded only data over the time Figure with supplements see all Download asset Open asset of leukocyte and infection in vitro and in Relative change (mean in (A) deformation and (B) size of in diluted whole blood after = see Figure supplement 1) and = 4 see Figure supplement in vitro measured and after and = 2 see Figure supplement 1) in vivo measured after Exemplary plots of size vs. deformation of in blood of a patient with (C) and (D) compared to controls of size and deformation of and lymphocytes in blood samples of patients with = 4 and = 6 and infection = relative to the norm (black, n = 21 donors as in Figure 1E with 68% and 95% confidence ellipses). and SD of For plots of size vs. deformation of all three cell types and all three disease conditions see Figure supplement Figure data 1 Source data for Figure Download We also found a in size and greater deformation of the at the time points in an healthy human from (Figure Figure supplement 1). blood in vitro with a and one of the major responsible for resulted in and more deformed measured between after blood (Figure Figure supplement 2). blood taken from patients with an of most likely bacterial had and more deformed compared to healthy controls (Figure The same neutrophil response was found in blood samples from patients with viral Figure monocytes by a size in both and patients and after in vitro with but showed a significantly deformation only in viral while blood lymphocytes not show any response (Figure Figure supplements 2 and The response when blood of patients with infection, which is to also the lymphatic both monocytes and lymphocytes showed an in cell size and deformation, while showed less of a response (Figure Figure supplement These results suggest that MORE blood analysis might be sensitive to bacterial from viral infections, and potentially other by the differential response of blood leukocyte populations. This possibility will be followed up in specific MORE blood analysis is of for blood tests in with patients at of but only of blood available for or to characterize in as it merely data Detection of morpho-rheological changes in malignant transformed blood cells Blood or both myeloid and cell are a further large area MORE analysis could potentially and While cell mechanics has been a focus of cell mechanics research and and 2009; and 2013), the mechanical properties of blood are The available research on mechanics of cells has been on cell or cells (Lichtman, 1973; Rosenbluth et al., 2006; Lam et al., 2008; Lichtman, 1970; Dombret et al., 1995; Lautenschläger et al., 2009; Ekpenyong et al., 2012; Rosenbluth et al., 2008; et al., 2015) but so far not directly in blood. MORE analysis of the blood of patients with myeloid and lymphatic revealed the new presence of cell populations — the characteristic not present in healthy donors (Figure Cell populations for revealed less deformed cells but of about the same size compared to healthy and myeloid cells (Figure Figure supplement in line with previous results et al., 2006; Lichtman, 1970; Dombret et al., 1995; Lautenschläger et al., 2009; Ekpenyong et al., 2012). cells were in size compared to but not show any in deformation (Figure Figure supplement 1). Since cell size and deformation in the channel are (Mietke et al., et al., et al., which can be seen by the the space (Figure we also the of cells (Figure supplement 1). results show that myeloid cells, and have increasing of with the findings of previous reports (Lichtman, 1973; Rosenbluth et al., 2006; Lam et al., 2008; Lichtman, 1970; Dombret et al., 1995; Lautenschläger et al., 2009; Ekpenyong et al., 2012; Rosenbluth et al., This is different than the in cells are found to be more than their healthy and 2009; and 2013). the differential stiffness of and and its further with has been implicated in the of et al., 2008; Rosenbluth et al., 2008; Lam et al., MORE analysis might not only screening for novel to cells et al., 2012; 2012; et al., 2015), but also assessing the of directly in each Figure 4 with 1 supplement see all Download asset Open asset Detection and of and of treatment (A) Normal brightness vs. size of a healthy donor with the used to identify lymphocytes monocytes and (B) Exemplary brightness vs. size in cells were found in and (C) Exemplary brightness vs. size in cells were found in and (D) of deformation and size for the in blood samples of (red n = 4 patients) and patients n =

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