Research ArticleCELL BIOLOGY

Cell sensing and decision-making in confinement: The role of TRPM7 in a tug of war between hydraulic pressure and cross-sectional area

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Science Advances  24 Jul 2019:
Vol. 5, no. 7, eaaw7243
DOI: 10.1126/sciadv.aaw7243
  • Fig. 1 Decision-making strategy and cell dynamics at trifurcating Ψ-like branch channels of different hydraulic resistances.

    (A) Phase contrast image of a Ψ-like trifurcating microfluidic device. The relative hydraulic resistance of each branch channel is indicated. Scale bar, 50 μm. (B) Absolute values of the hydraulic resistance of each branch channel shown in (A). (C) Distribution pattern of MDA-MB-231 and HT1080 cells in branch channels of different hydraulic resistances (n > 70 from three independent experiments for each cell line). Data represent the mean with 95% confidence interval. (D) Evaluation of protrusion dynamics of LifeAct-GFP H2B-mCherry MDA-MB-231 cells at the trifurcation. (i) Representative cell in the feeder channel before reaching the intersection. l0 is defined as the overall cell front-rear length. (ii) Representative cell at the intersection. lp is defined as the protrusion length inside the branch. (iii) Representative cell entering the right branch channel after decision is made. lp is defined as the distance between the leading edge of the cell and the nucleus. Scale bars, 20 μm. The contrast of the fluorescent signals has been increased in all images uniformly for visualization purposes. (E) Normalized protrusion length (lp/l0) for cells choosing the straight branch channel (n = 14) over time. Red line represents the moving average. At t0, cells first reached the intersection; at t1, the decision was made; and at t2, the nucleus fully entered the branch channel at the trifurcation. (F) Cell entry time, t2 − t1, in branch channels of different hydraulic resistances (n > 10 cells for each branch from >3 independent experiments). Data represent the means ± SD; Kruskal-Wallis with post hoc Dunn was performed, *P < 0.05 and ***P < 0.001 relative to the cells choosing the straight branch. (G) Normalized leading protrusion growth rate for cells entering branch channels of different hydraulic resistances (n > 10 for each branch from >3 independent experiments). Data represent the means ± SD; one-way analysis of variance (ANOVA) with post hoc Tukey was performed, *P < 0.05 relative to the cells choosing the straight branch.

  • Fig. 2 Cortical actomyosin regulates cell decision-making in response to hydraulic resistance.

    (A) Distribution pattern of MDA-MB-231 cells in branch channels of different hydraulic resistances in response to different pharmacological [(CK666 and Latrunculin A (LatA)] or molecular interventions (mDia1-KD) (n > 70 cells from three independent experiments for CK666 and mDia1-KD experiments and n > 20 cells from three independent experiments for LatA experiments). Data represent the mean with 95% confidence interval. P < 0.01 for the distribution of LatA-treated cells versus controls as indicated by χ2 test. (B) Decision-making times of MDA-MB-231 cells in response to different pharmacological (CK666 and LatA) or molecular interventions (mDia1-KD) (n > 30 cells from three independent experiments for each condition). Data represent the means ± SD. Mann-Whitney U test was performed, ****P < 0.0001 relative to scramble control (SC). Kruskal-Wallis with post hoc Dunn was performed, ****P < 0.0001 relative to vehicle control (VC). (C) Distribution pattern of MDA-MB-231 cells in branch channels of different hydraulic resistances in response to different pharmacological (blebbistatin) or molecular interventions (myosin-IIA or myosin-IIB KD) (n > 70 cells from three independent experiments for each condition). Data represent the mean with 95% confidence interval. P < 0.01 and P < 0.0001 for the distribution of blebbistatin-treated and myosin-IIA–KD cells, respectively, relative to controls as indicated by χ2 test. (D) Decision-making times of MDA-MB-231 cells in response to different pharmacological (blebbistatin) or molecular interventions (myosin-IIA or myosin-IIB KD) (n > 40 cells from three independent experiments for each condition). Data represent the means ± SD. Kruskal-Wallis with post hoc Dunn was performed, *P < 0.05 relative to scramble control. Mann-Whitney U test was performed, ****P < 0.0001 relative to vehicle control.

  • Fig. 3 Hydrostatic pressure induces intracellular calcium increase via TRPM7 activation.

    (A to C) Normalized calcium signal intensity of vehicle control and drug-treated or TRPM7 knockout (KO) MDA-MB-231 cells on 2D following the application of a 3-Pa hydrostatic pressure differential at t = 0 min (n > 30 cells from three independent experiments). All signal intensities are normalized to those of the respective unstimulated controls. Data represent means ± SEM. (D) Time course of whole-cell TRPM7 cationic currents recorded at +100 and −100 mV in a HEK293 cell transfected with mouse TRPM7 and exposed to a 30-Pa increase in hydrostatic pressure followed by exposure to 10 μM FTY720. (E) Current-voltage relationships of whole-cell cationic currents in HEK293 cells expressing mouse TRPM7 (top) or enhanced green fluorescent protein (EGFP; bottom) under basal conditions and after the application of a 30-Pa hydrostatic pressure differential in the presence or absence of FTY720. (F) Mean current densities measured under the different experimental conditions shown in (E). TRPM7-expressing cells (n = 8) and EGFP-expressing cells (n = 3). *P < 0.05, ***P < 0.002 for 30 Pa versus any other condition by Kruskal-Wallis followed by Dunn’s post hoc test. (G) Representative image sequence depicting the MIIA-GFP signal of an MDA-MB-231 cell before and after the application of a 3-Pa hydrostatic pressure differential. Pressure is applied right after (ii). Scale bar, 20 μm. (v) Kymograph of the line scan shown in (i) to (iv). The yellow arrow indicates the first frame after the application of a hydrostatic pressure differential. In (i) to (iv), following image segmentation, each pixel’s intensity value was assigned a color according to ImageJ’s fire heat map for visualization purposes. (H) Normalized cortex width and (I) normalized integrated cortical signal intensity of MIIA-GFP–labeled MDA-MB-231 cells following the application of a 3-Pa hydrostatic pressure differential at t = 0 min in response to vehicle control (n > 20 cells from three independent experiments), LatA, and/or FTY720 treatments (n > 5 cells for each condition from two experiments). Data are normalized to the initial (treated or untreated) values at t = −22 min before the application of hydrostatic pressure. (J) Normalized width and average fluorescence intensity of cortical actin and myosin measured by LifeAct-Ruby2– and MIIA-GFP–labeled MDA-MB-231 cells, respectively, immediately after the application of a 3-Pa hydrostatic pressure differential (n > 20 cells from three independent experiments). Data are normalized to the values right before the hydrostatic pressure exposure. Mann-Whitney U test was performed, ****P < 0.0001 relative to width of actin or myosin cortex before hydrostatic pressure exposure and #P < 0.05 relative to signal intensity of cortical myosin before the application of hydrostatic pressure.

  • Fig. 4 TRPM7 is a key mechanosensor that directs decision-making in channels of lower hydraulic resistance.

    (A) Representative images of calcium signal intensity of (i) control, (ii) TRPM7-KO, and (iii) para-nitroblebbistatin–treated cells once they first reached and occupied the entire intersection of 1×-0.6×-2.2× devices. Following image segmentation, oversaturated pixels indicating high calcium intensity within vacuoles/organelles were blackened, and then each pixel’s intensity value was assigned a color according to ImageJ’s fire heat map for visualization purposes. The borders of the microchannels are depicted by thin white lines. (B) Normalized calcium signal intensity within each protrusion of MDA-MB-231 control, TRPM7-KO, and para-nitroblebbistatin-treated cells in branches of different hydraulic resistances (n = 10 cells from >3 independent experiments). Data represent the means ± SD. Kruskal-Wallis with post hoc Tukey was performed. *P < 0.05, ****P < 0.0001 relative to cells in 1× channel. n.s., not statistically significant relative to 1× channel. (C) Distribution pattern of MDA-MB-231 cells in branch channels of different hydraulic resistances in response to different pharmacological (BaptaAM, 2-APB, and FTY720) or molecular interventions (TRPM7 KO) as compared to the pattern predicted by the branch channel cross-sectional area partition (n > 70 cells from three independent experiments for each condition). Data represent the mean with 95% confidence interval. P < 0.05 for all treatments relative to controls as indicated by χ2 test. (D) Phase contrast image of a Ψ-like trifurcating microfluidic device with branches of identical cross-sectional areas but distinct hydraulic resistances. The relative hydraulic resistance of each branch channel is indicated. Scale bar, 50 μm. (E) Distribution pattern of scramble control, TRPM7-KO, and MIIA-KD MDA-MB-231 cells in branch channels of the device shown in (D) (n > 70 cells from three independent experiments). Data represent the mean with 95% confidence interval. P < 0.05 for all treatments relative to controls as indicated by χ2 test. (F) Phase contrast image of a Ψ-like trifurcating microfluidic device with branches of identical hydraulic resistances but different cross-sectional areas. The cross-sectional area of each branch channel is indicated. Scale bar, 50 μm. (G) Distribution pattern of scramble control, TRPM7-KO, and MIIA-KD MDA-MB-231 cells in branch channels of the device shown in (F) (n > 60 cells from three independent experiments). Data represent the mean with 95% confidence interval. P < 0.05 for all treatments relative to controls as indicated by χ2 test.

  • Fig. 5 Cell distribution in branch channels of different hydraulic resistances and cross-sectional areas is described by MEP-based partition.

    (A) Representative image sequence depicting the cortical actin signal of a LifeAct-GFP–labeled MDA-MB-231 cell in each branch (dashed boxed area). Cell makes a decision right after (iv). The contrast of the fluorescent signals has been increased in all images uniformly for visualization purposes. (B) Normalized integrated actin signal intensity at the decision-making time point measured by LifeAct-GFP in scramble control and TRPM7-KO MDA-MB-231 cells (n > 10 cells, three independent experiments). Actin intensity was normalized to the signal of the left branch channel (1×). Data represent the means ± SD. One-way ANOVA with post hoc Tukey was performed. ****P < 0.0001 relative to control in 1× channel. ####P < 0.0001 relative to control in 0.6× channel. (C) Normalized integrated myosin-IIA signal intensity at the decision-making time point measured by MIIA-GFP in scramble control and TRPM7-KO MDA-MB-231 cells (n > 10 cells, three independent experiments). MIIA intensity was normalized to the signal of the left branch (1×). Data represent the means ± SD. One-way ANOVA with post hoc Tukey was performed. *P < 0.05 relative to control in 1× channel. ####P < 0.0001 relative to control in 2.2× channel. (D) Normalized integrated actin and myosin-IIA signal intensity at the decision-making time point measured in LifeAct-GFP– or MIIA-GFP–labeled MDA-MB-231 cells, respectively, as a function of hydraulic resistance (n > 10 cells for each data point). Inset: Normalized integrated actin signal intensity as a function of hydraulic resistance. Data are normalized to the 1× resistance and represent the means ± SD. (E) Prediction of probability distribution of MDA-MB-231 cells by cortical actin signal–based MEP partition in the trifurcating microfluidic device shown in Fig. 1A (1×-0.6×-2.2×) (n > 400 cells from >3 independent experiments). Data represent the mean with 95% confidence interval. (F) Prediction of probability distribution of MDA-MB-231 cells by cortical actin signal–based MEP partition in the trifurcating microfluidic device shown in fig. S1A (1×-0.6×-0.3×) (n > 300 cells from >3 independent experiments). Data represent the mean with 95% confidence interval. (G) Prediction of probability distribution of LatA-treated MDA-MB-231 cells by the surface energy MEP partition for the 1×-0.6×-2.2× microfluidic design (n > 20 cells from three independent experiments). Data represent the mean with 95% confidence interval. (H) Prediction of probability distribution of LatA-treated MDA-MB-231 cells by surface energy MEP partition for the 1×-0.6×-0.3× microfluidic design (n > 150 cells from three independent experiments). Data represent the mean with 95% confidence interval.

  • Fig. 6 Schematic summarizing how blebbing cells sense and respond to hydraulic resistance.

    Hydraulic resistance triggers TRPM7 activation in a magnitude-dependent manner, which, in turn, mediates calcium influx and supports a thicker cortical actomyosin meshwork, which preferentially directs cell entrance in low resistance channels. MIIA-GFP signal intensity correlates quantitatively with the hydraulic resistance of each branch channel, whereas the integrated LifeAct-GFP signal intensity correlates inversely with the cell distribution pattern into branches. Variations in bleb size and cortical actin intensity reach a minimum at the decision-making time point, suggesting a physical balance between internal and external cell forces. Inhibition of TRPM7 function or actomyosin contractility alters the decision-making pattern from hydraulic resistance–based to cross-sectional area–based partition.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/7/eaaw7243/DC1

    Fig. S1. Characterization of cell decision-making at trifurcating Ψ-like branch channels of different hydraulic resistances.

    Fig. S2. Effects of different cytoskeletal constituents on cell decision-making in response to hydraulic resistance.

    Fig. S3. Hydrostatic pressure induces intracellular calcium increase and a thicker cortical actin meshwork.

    Fig. S4. High hydraulic resistance results in larger changes of membrane curvature.

    Fig. S5. Variations in bleb size and cortical actin intensity reach a minimum at the decision-making time point, suggesting a physical balance between internal and external cell forces.

    Table S1. Dimensions of the different trifurcated designs.

    Table S2. Comparison between the experimental and theoretical probabilities of cells entering branches in two different devices.

    Table S3. Comparison between the experimental and theoretical probabilities of cells entering branches in multiple devices.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Characterization of cell decision-making at trifurcating Ψ-like branch channels of different hydraulic resistances.
    • Fig. S2. Effects of different cytoskeletal constituents on cell decision-making in response to hydraulic resistance.
    • Fig. S3. Hydrostatic pressure induces intracellular calcium increase and a thicker cortical actin meshwork.
    • Fig. S4. High hydraulic resistance results in larger changes of membrane curvature.
    • Fig. S5. Variations in bleb size and cortical actin intensity reach a minimum at the decision-making time point, suggesting a physical balance between internal and external cell forces.
    • Table S1. Dimensions of the different trifurcated designs.
    • Table S2. Comparison between the experimental and theoretical probabilities of cells entering branches in two different devices.
    • Table S3. Comparison between the experimental and theoretical probabilities of cells entering branches in multiple devices.

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