Nature Methods: doi: /nmeth Supplementary Figure 1. Microfluidic chip layout.

Transcription

1 Supplementary Figure 1 Microfluidic chip layout. Positions where inlet and outlet tubing are connected are marked with. Arrays of pillars filter out debris and dust particles to avoid clogging of the narrow channel. The region of interest of the camera is positioned at the end of the 300 µm long channel during the measurement (measurement region).

2 Supplementary Figure 2 Principle and characterization of the fluorescence detection of RT-FDC. (a) Light sheet excitation and detection of fluorescence results in fluorescence peaks with widths depending on fluorophore localization. (b) Aperture function in focal plane (x-y) of excitation and detection for the three fluorescence channels/lasers. (c) Determination of thickness of the light sheet along the channel axis x yielded values of 3.64 ± 0.03 µm for 488 nm, 3.16 ± 0.02 µm for 561 nm, and 3.41 ± 0.05 µm for 640 nm. Thus, the line width is smaller than most eukaryotic cells and it can be used to identify sub-cellular distribution of fluorescent probes. (d) Measurement of two-color fluorescent agarose beads with heterogeneous sizes. Plotted are the fluorescence peak widths of the green (FL-1: 525/50 nm) and red (FL-3: 700/75 nm) channels. The linear fit indicates that the system determines nearly identical sizes in both channels. Experiments a-c were repeated three times with similar results.

3 Supplementary Figure 3 Calibration bead measurements. Calibration beads with 8 populations were measured with RT-FDC and FCM (BD LSR II) for all three channels. (a-c) Fluorescence intensity histograms from RT-FDC measurements. Small labels in graph denote the peak position. (a) Green FL-1 channel (525/50 nm), (b) red FL-2 channel (593/46 nm), and (c) deep red FL-3 channel (700/75 nm). (d) Normalized sub-population intensities are compared versus peak or population index. (e-g) Histograms from LSR II FCM data for corresponding channels. (e) Green channel (530/30 nm), (f) red channel (575/26 nm), and (g) deep red channel (670/14 nm). Both methods consistently identify eight populations of objects with intensities over a dynamic range of three orders of magnitude. Experiments a-b, e-g were repeated three times with similar results.

4 Supplementary Figure 4 Evaluation of CD34 + subpopulation regarding image-processing-based parameters deformation, size, brightness, and granularity. CD34 - cells are plotted in gray while CD34 + cells are shown in colors that encode density of data points. (a) Deformation of CD34 + HSPCs vs. CD34-APC fluorescence intensity. (b) Projected area vs. CD34-APC fluorescence intensity. (c-d) Image-based brightness evaluation. (c) Brightness inside cell contour plotted over the CD34-APC intensity. Brightness can be used as an additional marker to identify subsets of leukocytes, as recently shown in Toepfner et al. 11. (d) Granularity is characterized by the standard deviation of the pixel brightness values inside the cell contour, and plotted over CD34-APC intensity. There was no correlation of CD34-expression level detectable with either parameter. However, the CD34-positive HSPCs seem to have relatively low spread in the brightness parameters compared to the very heterogeneous CD34-negative cells. Experiments (a-d) were repeated three times independently with similar results.

5 Supplementary Figure 5 Three-color antibody staining of HSPCs. Blood samples from GSF mobilized donors were collected after apheresis and stained with CD3-FITC, CD34-PE, and CD14-APC. (a) Gating strategy for exclusion of RBCs by cell size and brightness. (b) Gate for CD34-positive cells by fluorescence intensity. (c) Gate for CD3 CD14 subpopulation for exclusion by lineage markers. (d) Resulting mechanical fingerprint for CD34 + CD3 CD14 subpopulation. (e) Back-gated subpopulation from (d) in initial representation. (f) CD3 + fraction in mechanical fingerprint. (g) CD14 + fraction. (h) CD34 + fraction with all other cells shown in gray. Experiment was performed once.

6 Supplementary Figure 6 Representative flow cytometry measurement of CD34 APC-stained G-CSF-mobilized blood samples. (a) Forward scatter vs. side scatter plot with gate 1 to separate cell events from debris. (b) Fluorescently labeled CD34 + cells are found in gate 2 in the side scatter vs. APC intensity plot. (c) Histogram of fluorescence peak heights for cells of gate 1 (gate 3 = CD34 + ). Experiment was performed three times independently with similar results.

7 Supplementary Figure 7 The mechanical properties of immature versus mature red blood cells (RBCs). Reticulocytes are the immature fraction of RBCs and represent normally about 5 15 of the total number of RBCs in circulating human blood 12. During the first two days after being released to the blood stream, reticulocytes lose their cytoplasmic RNA as they mature to RBCs. This enabled us to use nucleic acid staining with syto13 to identify RNA-positive reticulocytes by fluorescence. Fluorescence read-out is compared between commercial LSR II FCM (a) and RT-FDC (b) as shown in the histograms. (c) Comparison of reticulocyte fraction determined with RT-FDC and state-of-the-art automatic blood count (XE-5000, Sysmex Deutschland) for three donors. Columns show counts, error bars represent standard error resulting from Poisson statistics. Reticulocyte numbers relative to RBCs were in agreement. (d) Location of RBC and reticulocyte population medians in deformation vs. area plot. (Shaded regions are for illustration purposes) Red blood cells have median size of µm 2 and deformation of while reticulocytes are larger (39.2 µm 2 ) and show smaller deformation (0.240). The difference indicated by arrows was significant as tested with linear mixed model analysis (Supplementary Table 2). (e,f) Representative deformation vs. area plots of reticulocytes and mature RBCs. (g) Deformation vs. syto13 fluorescence, and (h) area vs. syto13 fluorescence. Results are in line with previous reports on reticulocytes 13, which had been restricted to labor-intensive micropipette and micro-pore assays with limited number of cells. We found human reticulocytes in whole blood samples to be slightly larger and less deformed compared to RBCs (d, Supplementary Table 2). Increased reticulocytosis has been linked with increased morbidity in infants with sickle cell anemia 14 and RBC distribution width is also used as a predictor of poor clinical outcome in settings of various diseases including coronary heart disease 15. Adding RT-FDC phenotyping to the reticulocyte indices in clinical use could therefore provide a means of monitoring changes of blood rheology in haematological diseases 11,15. Experiments (a,b) were performed once, (c-h) were performed with samples from three different donors.

8 Supplementary Figure 8 1D-fluorophore localization detection by fluorescence peak width. We used 1D imaging to detect the subcellular, nuclear localization of a fluorescent protein by comparing fluorescence peak widths for cells expressing nuclear localization sequence (nls) tagged and cytoplasmic (cyt) fluorescent proteins. Here, HeLa cells were transfected with plasmids expressing GFP and mrfp with and without nuclear locator sequence (nls). Red dots: cytosolic RFP and nuclear GFP, black dots: both fluorophores cytosolic, green dots cytosolic GFP and nuclear RFP. Cells where GFP and RFP fluorophores co-localize can be found on the diagonal of this plot. Since, in animal cells, cell cycle progression involves changes in nuclear permeability, we were able to exploit this feature to identify mitotic cells in heterogeneous population to identify mitotic cells in the Drosophila RNAi screen (see Fig. 2c). This experiment also serves to show that the green and red channel can be used interchangeably for this kind of analysis. Experiments were performed 3 times independently with similar results.

9 Supplementary Figure 9 Effect of colchicine treatment on fraction of mitotic Kc167 cells. Fluorescence peak width plots (tdtomato vs. nls-gfp) for (a) non-treated or (b) colchicine treated cells show that colchicine treatment increases the number of cells with cytosolic nls-gfp (fraction of cells with even peak widths, localized along diagonal). During mitotic entry, the nuclear envelope breaks down releasing the nls-gfp protein to the cytosol, which affects the measured peak width in the GFP channel. This is exploited to identify mitotic cells in RT-FDC measurements. Mitotic cells represented by colored dots, interphase cells shown in gray. (c) Comparison with manually inspected confocal microscopy images of Hoechst33342 stained cells. Bar graphs show percentages obtained from counting, error bars indicate standard error resulting from Poisson statistics. For microcopy non-sync. 49/1063 (mitotic/total) cells were analyzed, for microscopy sync. 156/928 cells, and for RT-FDC non-sync. 388/6871 and 1078/6120 cells were analyzed. Experiments (a,b,c) were performed once.

10 Supplementary Figure 10 Data processing of Kc167 RNAi screen. For each gene, experiments with two dsrna variants were performed, each repeated on three different days and two different flowrates. Exemplary graphs for gene 10 (Rok) at 0.04 µl/s flow-rate are shown. The statistical analysis for this and all other sets of

11 measurements is available in Supplementary Table 3. Mitotic cells are represented by colored dots while limited number of interphase cells is shown in gray. All experiments (center column) were compared against a mock control (left column) from the same day. See right column for comparison of the scatter plots. Dashed contour lines are drawn at 50 % of the maximum density of data points, and solid contour lines at 95 %. Data from mock and treatment were transformed into elastic modulus values for comparison by the linear mixed model analysis (Supplementary Fig. 10 and Supplementary Table 3).

12 Supplementary Figure 11 Results of the screen for genes regulating mitotic cell mechanics in Kc167 cells. For each gene two dsrna constructs were used and each dsrna treatment was repeated three times on separate days. The size and deformation data was transformed into E modulus using Shapeout software and statistical analysis was performed using a linear mixed model. Genes with consistent results for both flowrates and both p-values <0.05 were considered as hits. ( * : p<0.05, ** : p<0.01)

13 Supplementary Figure 12 Two-color fluorescence detection of FUCCI cell-cycle marker 16. (a) Two color fluorescence scatter plot with gates for G1, S and G2/M phase. (b) A pulse width evaluation enables detection of mitotic cells because of GFP localization inside the nucleus during interphase and in the cytosol during mitosis, resulting in wider pulses. (c-f) Cell cycle specific mechanical fingerprints for the mixed sample for G1, S, G2 and mitosis. Experiments were performed 10 times independently with similar results.

14 Supplementary Figure 13 Effect of suspension on RPE1 cells. (a) Cell cycle phase-specific mechanical fingerprint of RPE1 cells directly after being detached from plate using trypsin (cyan), and after 30 minutes in suspension (magenta). Contours show 95 % density iso-lines, dashed contours 50 % iso-lines. (b) Comparison of deformation medians of N=10 replicates. In interphase the cells show a significant change in deformability due to being kept in suspension for 30 min. The mitotic subpopulation on the other hand, does not change significantly based on t-test (two-sided, Welch corrected) with **: p<0.01 and n.s.: p Box plot whiskers range from 5 th to 95 th percentiles, box from 25 th to 75 th percentile, horizontal line denotes the median, and mean is symbolized by a small square. N=10 replicates with samples from the same cell culture but treated and measured independently on different days, were analyzed.

15 Supplementary Figure 14 Technical details of the alignment light path. For (a) excitation and (b) emission light paths of the RT-FDC setup. For an explanation of the various parts, see Online Methods, section Experimental Setup.

16 Supplementary Material Real-time fluorescence and deformability cytometry Supplementary discussion of RNAi screening results The combined advantage of detecting the 1D-spatial distribution of specific fluorescent molecular markers and high throughput mechanical phenotyping of RT-FDC opens up the new possibility of conducting large-scale screens for regulators of cell mechanics involved in particular cellular functions. Here we exemplarily used RT-FDC to identify regulators of cell mechanics of mitotic cells in the Drosophila cell line Kc167. These are ideal for such an analysis because I) they grow at room temperature, II) they have low genetic redundancy relative to human cells, III) RNAi is highly efficient in these cells, IV) they can grow in suspension 1. However, despite the utility of the system for screening cell phenotypes, using existing technology such as RT-DC and AFM, it has not been possible to screen large numbers of cells and specifically identify cells of rare populations (as mitotic cells). Thus, in order to carry out such a screen, we needed a way of identifying the small percentage of cells that are present in mitosis. To do so, we generated a novel Kc167 cell line expressing nls-gfp and cytoplasmic tdtomato. This enables the identification of mitotic cells on the basis of having similar nls-gfp and cytoplasmic tdtomato fluorescence peak widths (see Fig. 2a), something only apparent after permeabilization of the nuclear envelope which happens at the mitotic entry. Using this cell line, we then characterized mechanical changes in this subpopulation with knockdown of 42 selected genes (see Supplementary Tables 3,5). The set of genes was chosen to test the impact of silencing genes that represent positive and negative regulators of the Rho pathway, which has been implicated in the control of mitotic cell cortex mechanics 2. Each knock-down line was measured in three independent repeats and data for two flowrates was collected (see Online Methods). A linear mixed model 3 was then used to compare treatment and control samples, because it allows for disentangling the effects of the treatment from effects of random variation (Supplementary Fig. 10) present in measurements of biological repeats of the sample. Causes for this variation include differences in knock down efficiency between the repeats but also between the different dsrnas used. Day-to-day variations, such as the experimental room temperature might also affect the deformation that we measure. These day-to-day variations were considered by comparing each sample to a control measured on the same day, while random experimental influences are considered by the linear mixed model analysis. Higher flow rates cause higher stresses on the cells. So, as an additional precaution, only effects consistently appearing for a higher and a lower flow rate (0.04 µl/s and 0.06 µl/s) and so for two different stresses were considered in the analysis. Cells were synchronized in prometaphase with colchicine treatment, which enriched the mitotic population (Supplementary Fig. 9), without changing the mechanical phenotype of mitotic cells (Supplementary Table 4). To compensate for potential cell size changes due to gene depletions, we utilized a numerical model 4 for calculating elastic modulus for cells deformed in RT-FDC. A linear mixed model 3 was then used to compare treatment

17 and control samples. The analysis pipeline and all results are shown in Supplementary Fig. 10 and Table 3 respectively, and the summary of the results for elastic modulus changes are shown in Supplementary Fig. 11. Here we discuss the results in some detail. Consistent with known functions of Rho in mitosis 5, we observed a drastic decrease in elastic modulus of mitotic cells after depletion of Rho1. This was the only small GTPase of the Rho family whose depletion significantly altered mitotic cell mechanics (Supplementary Fig. 11). Activation of Rho depends on the activity of Guanine nucleotide Exchange Factors (GEFs) while the protein is deactivated by GTPase Activating Proteins (GAPs). Thus, depletion of Rho GEFs responsible for activating Rho1 would be expected to mimic the rho1-kd phenotype, while depletion of Rho GAPs would be expected to have the opposite effect to that of Rho1 depletion. We observed that knock-down of the pebble gene resulted in highly compliant mitotic cells which is in line with existing data 6. On the other hand, depletion of other GEF proteins (namely RhoGEF2, RhoGEF3 and RhoGEF4) did not reproduce the phenotype of Rho1 depletion. We also found that depletion of six different GAP proteins showed a statistically significant increase in cells elastic modulus. This may suggest a redundant role for the GAP proteins in regulating Rho signaling in mitotic Drosophila cells. We note that a RhoGAP protein which is believed to regulate mitotic cells contractility in human cell lines and in C. elegans has no homologue in flies 7. Downstream of Rho1 activation, a molecular signaling cascade was identified that appears to mediate its impact on the cytoskeleton 2,8 10. In our screen, the elastic modulus of mitotic cells was significantly lower than of control cells after the depletion of Rho Associated Kinase (drok), Myosin Light Chain protein, and Myosin II Heavy Chain protein. Moesin which was shown to also regulate properties of mitotic cells 8 in our screen had an impact, but only visible in one flowrate, while the change was less significant in second flowrate (p>0.05). When actin regulators were considered, we detected changes in mitotic cell mechanics upon depletion of Cofilin and Diaphanous proteins, both previously implicated in regulating mitotic cells mechanics 9,10. Interestingly we noted a clear change in the phenotype of cells depleted of Cofilin protein, however the cells appeared stiffer than the control cells, which is in contrary to results published by Chugh P and coworkers 10. This may be due to the presence of unusual cell protrusions and shapes present in strains depleted of Cofilin protein. We detected no significant changes following depletion of other actin regulators (Supplementary Fig. 11), which is consistent with the results published recently where actin cytoskeleton regulators were investigated for their ability to change thickness of the mitotic cells cortex 10. All together the data collected for this screen is in good agreement with data obtained by other methods for measuring cell mechanics of mitotic cells. We were able to detect changes that are a result of disrupting Rho signaling, in both positive and negative directions through changes to both actin and/or myosin II, and were able to demonstrate a novel role for the RhoGAP proteins to the negative regulation of mitotic cell mechanics.

18 Supplementary Tables Pos (mech.) Neg (mech.) Pos (fl.) 69% (n=422) 31% (n=189) Neg (fl.) 9% (n=10.090) 91% (n= ) Supplementary Table 1 Confusion matrix for identification of CD34 + positive cells using gating by mechanical phenotype. Parameter Unit Intercept Int. Std. Treatment Tr. Std. p-value Area µm² Deformation Supplementary Table 2 Linear mixed model evaluation for properties of red blood cells ( Intercept ) and reticulocytes ( Treatment ). The Intercept of the LMM is an estimate of the mean value of the control group, which has a standard error ( Int. Std. ). The column Treatment shows the difference between control and tested group, its associated error ( Tr. Std. ), and the significance of the difference ( p-value ). The table shows that reticulocytes are significantly larger and less deformable. Samples from three different donors were measured independently. p<0.05 is considered significant. Gene symbol Flowrate (ul/s) Intercept (kpa) Int. Std. (kpa) Treatmen t (kpa) Tr. Std. (kpa) p-value Moe sqh Rok E zip dia pbl scra

19 Rac RhoGAP1a RhoGAP54d RhoGAPp Cdc Rho capt cpb flr SCAR tsr alpha-spec beta-spec Tm conu Rac RhoGEF RhoGEF RhoGEF RhoGAP93B

20 RhoGAP92B RhoGAP71E RhoGAP68F RhoGAP5A RhoGAP19D RhoGAP18B RhoGAP16F RhoGAP15B RhoGAP A RhoGAP100 F RhoL RhoBTB Mtl Miro RhoU Supplementary Table 3 Linear mixed model evaluation of mechanical properties (elastic modulus) of control mitotic Kc167 cells ( Intercept ) and knock down cells ( Treatment ). Column description as in Supplementary Table 2. Results for two flow rates for each of the 42 gene knock-downs are represented in rows. The data is summarized in Fig. 2e. For each gene 2 independent dsrna constructs were used and each dsrna treatment was performed thrice on different day each. The data was

21 analyzed using linear mixed model analysis. Genes with consistent results for both flowrates and both p-values <0.05 were considered as hits.

22 Parameter Unit Intercept Int. Std. Treatment Tr. Std. p-value Area µm² Deformation E-modulus kpa Supplementary Table 4 Linear mixed model evaluation for properties of nonsynchronized mitotic Kc17 cells ( Intercept ) and colchicine treated mitotic Kc167 cells (Treatment). Description of columns as in Supplementary Table 2. While there is a significant change in size and deformation caused by colchicine treatment, the elastic modulus is not affected. This might appear counterintuitive. However, larger cells experience more stress and thus should deform more, even though their mechanical properties are identical. The size change is likely due to the fact that cells continue to grow when they are blocked in cell cycle. For this test two samples from same cell culture treated independently on three different days for each condition (treated/untreated) were measured. p<0.05 was considered significant. Gene symbol RhoA Mtl Rac2 RhoL Miro cdc42 Primer name Primer sequence taatacgactcactatagggagcaaaaatgaagcaggagc taatacgactcactataggggagcaaaaggcatctggtct taatacgactcactataggggacgacgattcgcaagaaattg taatacgactcactatagggctgtttgccatccacctcg taatacgactcactatagggcagtacacagctatcggcaca taatacgactcactatagggcacatattcgccgggaaag taatacgactcactatagggacacaatacaggtctcgctgg taatacgactcactatagggatcgggacagtggtgcttta taatacgactcactatagggcgaagcattcaaaaactgtctg taatacgactcactatagggatgggttcagcagctcgat taatacgactcactataggggagtgtgccgataatcctgg taatacgactcactatagggctccagatacttgaccgcag taatacgactcactataggggcccgctgaaaataaccat taatacgactcactatagggcagtgtcccagagggtcaga taatacgactcactatagggaccaactgcttcctgttgtg taatacgactcactataggggtggtcacgaacttctccga taatacgactcactatagggacatccgcctgcaagaagt taatacgactcactatagggtttttctgtatcacggccttc taatacgactcactatagggggatccgatgtttttggatg taatacgactcactataggggctgccacactaggcgac taatacgactcactataggggagaagctggccaagaacaa taatacgactcactataggggcgtgtgaggaaagcagaat taatacgactcactatagggcgctcctcctcgttgataag

23 taatacgactcactataggggtactccgacgggaacttgt RhoU taatacgactcactatagggccaccaaatgcgaatatgaa taatacgactcactataggggtctctgcgaattgttatacgg taatacgactcactatagggcgatcgtttcgttcgtttg taatacgactcactatagggcacttgcaacgtttgaatttct RhoBTB taatacgactcactataggggagcagccgcatcaagag taatacgactcactatagggatgcgatactggtcaatggc taatacgactcactatagggatgtggtgctgctgtgctt taatacgactcactatagggaacaaaggttcccttctcgc Rac1 taatacgactcactatagggcggtcacactgcagtacaca taatacgactcactatagggtggactggagtgctatgtgc taatacgactcactatagggccaagtggtatccggaggt taatacgactcactatagggtccagatacttgaccgctcc pebble taatacgactcactatagggggccgcatttagagctgat taatacgactcactataggggccttcctatcgacaggtgc taatacgactcactatagggtggaagagttggctgaaacc taatacgactcactataggggacgtatggtggatacgcct RhoGEF 4 taatacgactcactatagggcaacaccgttgtggaaaactc taatacgactcactataggggatgaagcccacccaagtg taatacgactcactatagggagcaacaacagcagtccgat taatacgactcactatagggcgcaggaattctccgttaag RhoGEF 2 taatacgactcactatagggcacatatcaaaacccattggaa taatacgactcactatagggccagtcaccagaagtggttg taatacgactcactataggggacgaacatgaatacgatgagg taatacgactcactatagggaattactggtgactgtggcg RhoGEF 3 taatacgactcactataggggctgaggaagccactgctc taatacgactcactatagggggcaacaaagtccacactgg taatacgactcactatagggcccttatttggcgttcttcc taatacgactcactatagggcgacaccacacgatccatt RhoGA P71E taatacgactcactataggggccgacgatgatgaggac taatacgactcactataggggctgctgttgttggtagtgg taatacgactcactatagggaaaaggcatagcagctccg taatacgactcactatagggctttgaaaagccagctttgatt RhoGA P93B taatacgactcactataggggccaccacagcaaatccta taatacgactcactatagggtccgctcttggttgcat taatacgactcactatagggcagcacatcttcagcgcata taatacgactcactatagggatgacataatgcactggccata RhoGA P18B taatacgactcactatagggggagtgcgccataagttgtc taatacgactcactatagggaggcttagcgtttcatccg taatacgactcactataggggagtgtggaaacccagatcc taatacgactcactataggggtcactggcacaatcctcaa

24 RhoGA P102A taatacgactcactatagggtggtatccgtcggaacattt taatacgactcactatagggacaatactgagcgaggattctga taatacgactcactatagggttctgttgatacatgcccaca taatacgactcactataggggtcaaatgagtgcttcgcaa RhoGA P92B taatacgactcactataggggtcacacccgcctcacttt taatacgactcactatagggtgacagattttcggctgcta taatacgactcactatagggcgagctggagaagacgatg taatacgactcactatagggctgcgcttcagcgaggtat RhoGA P54d taatacgactcactatagggcactggttaggtagcgtctcg taatacgactcactatagggaataacgactgccaacggat taatacgactcactatagggacaagatgagcgccgataac taatacgactcactatagggggcgacatggttttctttct RhoGA P100F taatacgactcactatagggaaggacccggtgatgctac taatacgactcactatagggccttgttcatcagcttgtgg taatacgactcactatagggatgggtgagcctccaaact taatacgactcactatagggccaggctgtccaggatagaa RhoGA P5A taatacgactcactataggggagttctgcgccaacttctt taatacgactcactatagggggtcagatccgtgccaaatac taatacgactcactatagggctatacggctccgagtacca taatacgactcactatagggcgggcttatagaggagcttg RhoGA P16F taatacgactcactatagggataaccaaggtgccgctg taatacgactcactataggggatcatcagcttgacgcagt taatacgactcactatagggacacttgacgtcactgggaa taatacgactcactataggggctatcgggttgttcgcaa RhoGA P68F taatacgactcactatagggccgcatacattctacatgcc taatacgactcactataggggtaatctcggcgcgaaac taatacgactcactataggggaacttgagcccgaggaag taatacgactcactatagggcacaaatccttccagttggg RhoGA P15B taatacgactcactatagggcctctatccagagctgcgtc taatacgactcactatagggggctcctggacattctcatt taatacgactcactataggggctgatctccgccaagaat taatacgactcactataggggcctcctgattggcatacaa RhoGA P19D taatacgactcactataggggccattatcctccagctcct taatacgactcactataggggtccaggtgcagatgaagc taatacgactcactatagggtaccaagtgaaagcgatgga taatacgactcactatagggatctgcaggacatcgcact rhogap 1a taatacgactcactatagggatcggatcggaaaaggtgag taatacgactcactatagggtcgacatcgttttctagacgc taatacgactcactataggggactggcggagatgtttgac taatacgactcactataggggtttcgcgtagcaaatcgtt conu taatacgactcactatagggctgcttccgcctgaaaataga

25 taatacgactcactatagggcactgagcggccattcttac taatacgactcactatagggtctaaaaaggacttggaaccca taatacgactcactatagggtatcaggtttccgttttggc rhogapp 190 taatacgactcactataggggagggcgtcgagtaccagt taatacgactcactatagggcatcgggcatgactttctg taatacgactcactatagggacttcagcggatccacacat taatacgactcactataggggctgaccagctggaagttct zip taatacgactcactatagggtggaggaagccgaactaaaa taatacgactcactatagggatttcttgcatttgggtggt taatacgactcactataggggtgagcgccgtcctactg taatacgactcactataggggaactccacctgctcctttg sqh taatacgactcactatagggcacacacatcactccagcg taatacgactcactatagggaatctgcgcctgatcgaa taatacgactcactatagggcgactacctcgaattcacgc taatacgactcactatagggctggcaccaacatctcacac Rok taatacgactcactataggggatacacaaggagcatgtggac taatacgactcactataggggatatcttgctgccatcggt taatacgactcactatagggttggcaatgcagatcaatca taatacgactcactataggggaacgatccattcggagttg dia taatacgactcactatagggctggacagtctgttcggaag taatacgactcactatagggcctcttgtccttcggaatgt taatacgactcactataggggacgattgagaagctgctgg taatacgactcactatagggagcagcttgagggcttcac Moe taatacgactcactatagggaaactttagcgtgcattcgc taatacgactcactatagggctgcaggtagaacagacgca taatacgactcactataggggaaacaagatttggcgcagt taatacgactcactatagggcgcactatcgatctctgatctt tsr taatacgactcactataggggctccttcgatgctctcaag taatacgactcactatagggcaaattggcgatctcaacag taatacgactcactatagggtgtgaaagcgaaaaactacagg taatacgactcactataggggatctgccacagtctccaca Tm1 taatacgactcactataggggcagtcgggcaaaacagt taatacgactcactataggggatgacaggcccgaagatt taatacgactcactatagggcgacctcgtcctggagaa taatacgactcactatagggccaaaaatatagctgtgcgga scra taatacgactcactatagggaccctgccaaatacgacaaa taatacgactcactatagggaatgcaaatgcgagttgtga taatacgactcactatagggctcgagaaggcggaacag taatacgactcactatagggggcttgtccgtggttatgtt capt taatacgactcactatagggcaactgcagtacgtgacgct taatacgactcactataggggggaatgctttcgctgatg

26 cpb α-spec β-spec flr SCAR taatacgactcactatagggggcaagaccctgaagactgt taatacgactcactataggggagccaggacagcaatgttt taatacgactcactatagggcccagcagatcgagaagaac taatacgactcactatagggagtccccatcccggttatag taatacgactcactatagggaactggaaatcgaggcgaac taatacgactcactatagggctgtacctcgaccacatgga taatacgactcactatagggagacaagtaaacgaaccagcg taatacgactcactatagggcgagtcctcgagcttttcac taatacgactcactatagggctgcagctggagcagaactt taatacgactcactatagggcgttaatggccttgtcgaag taatacgactcactatagggttctgaaggcaacacattcg taatacgactcactataggggggatctacggtttccacag taatacgactcactatagggaggtgcacaccaaggagaagg taatacgactcactataggggccatctgggtctggataac taatacgactcactatagggcctttggacagtggagtcg taatacgactcactatagggacggtgatgggcttattgtg taatacgactcactatagggaagattcgcatttgggacac taatacgactcactataggggtgccggtttcagacataaag taatacgactcactatagggagtgtacgatacacgtccgc taatacgactcactataggggcatgcgactgatacattcc taatacgactcactatagggtacgatacacgtccgccac taatacgactcactatagggcatgcgactgatacattccg Supplementary Table 5 Primer sequences used to generate dsrna reagents for the Drosophila screen.

27 Supplementary References 1. Baum, B. & Cherbas, L. Drosophila cell lines as model systems and as an experimental tool. Methods Mol. Biol. 420, (2008). 2. Ramanathan, S. P. et al. Cdk1-dependent mitotic enrichment of cortical myosin II promotes cell rounding against confinement. Nat. Cell Biol. 17, (2015). 3. Herbig, M. et al. in Flow Cytometry Protocols (eds. Hawley, R. & Hawley, T.) (Humana Press, 2017). doi: / Mokbel, M. et al. Numerical Simulation of Real-Time Deformability Cytometry To Extract Cell Mechanical Properties. ACS Biomater. Sci. Eng. acsbiomaterials.6b00558 (2017). doi: /acsbiomaterials.6b Maddox, A. S. & Burridge, K. RhoA is required for cortical retraction and rigidity during mitotic cell rounding. J. Cell Biol. 160, (2003). 6. Matthews, H. K. et al. Changes in Ect2 localization couple actomyosin-dependent cell shape changes to mitotic progression. Dev. Cell 23, (2012). 7. Zanin, E. et al. A conserved RhoGAP limits M phase contractility and coordinates with microtubule asters to confine RhoA during Cytokinesis. Dev. Cell 26, (2013). 8. Kunda, P., Pelling, A. E., Liu, T. & Baum, B. Moesin controls cortical rigidity, cell rounding, and spindle morphogenesis during mitosis. Curr. Biol. 18, (2008). 9. Bovellan, M. et al. Cellular Control of Cortical Actin Nucleation. Curr. Biol. 24, (2014). 10. Chugh, P. et al. Actin cortex architecture regulates cell surface tension. Nat. Cell Biol. 19, (2017). 11. Toepfner, N. et al. Detection of human disease conditions by single-cell morphorheological phenotyping of blood. Elife 7, e29213 (2018). 12. d Onofrio, G. et al. Simultaneous Measurement of Reticulocyte and Red Blood Cell Indices in Healthy Subjects and Patients With Microcytic and Macrocytic Anemia. Blood 85, (1995). 13. Waugh, R. E. Reticulocyte rigidity and passage through endothelial-like pores. Blood 78, (1991). 14. Meier, E. R. et al. Increased Reticulocytosis during Infancy Is Associated with Increased Hospitalizations in Sickle Cell Anemia Patients during the First Three Years of Life. PLoS One 8, (2013). 15. Felker, G. M. et al. Red Cell Distribution Width as a Novel Prognostic Marker in Heart Failure: Data From the CHARM Program and the Duke Databank. J. Am. Coll. Cardiol. 50, (2007). 16. Sakaue-Sawano, A. et al. Visualizing Spatiotemporal Dynamics of Multicellular Cell-Cycle Progression. Cell 132, (2008).