Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements

Single cells are dynamic, viscoelastic materials¹. A multitude of internal and external processes, (e.g., onset of mitosis or remodeling of the extracellular matrix [ECM]), influence their structure and composition^2,3,4, often resulting in distinct biophysical properties that complement their current state. In particular, mechanical properties have been shown to be important biomarkers of cellular development, physiology, and pathology, yielding valuable quantitative information that can supplement canonical molecular and genetic approaches^5,6,7. For example, Li et al. recently described the mechanical differences between drug-resistant and drug-responsive acute promyelocytic leukemia cells, while also using RNA-seq to uncover differentially-expressed cytoskeleton-associated genes⁸. By understanding the complex interplay between single-cell mechanics and cellular function, mechanophenotyping has broader applications in transforming basic science and clinical diagnostics⁹.

The most widely adopted tool for measuring single-cell mechanics is atomic force microscopy (AFM). While AFM enables a high-resolution, localized measurement of cellular mechanical properties, it remains limited to a throughput of <0.01 cells/s¹⁰. Alternatively, optical stretchers, which use two divergent laser beams to trap and deform suspended single cells¹¹, are limited to marginally higher throughputs of <1 cell/s¹². Recent advances in microfluidic technologies have enabled a new generation of devices for rapid, single-cell, mechanical assessment^12,13. These techniques employ narrow constriction channels^14,15, shear flow¹⁶, or hydrodynamic stretching¹⁷ to deform cells quickly at throughputs of 10-1,000 cells/s¹⁸. While the measurement rate of these approaches is considerably faster than conventional techniques, they often trade high-throughput capabilities for limited mechanical readouts (Supplementary Table 1). All the aforementioned rapid microfluidic methods focus on basic, single-parameter metrics, such as transit time or deformability ratios, that only reflect a cell's elastic properties. However, given the intrinsic viscoelastic nature of single cells, a robust and thorough mechanical characterization of cells requires consideration of not only elastic components but also viscous responses.

Mechano-node-pore sensing (mechano-NPS)^2,8 (Figure 1A) is a microfluidic platform that addresses existing limitations with single-cell mechanophenotyping. This method enables the measurement of multiple biophysical parameters simultaneously, including cell diameter, relative deformability, and recovery time from deformation, with a moderate throughput of 1-10 cells/s. This technique is based on node-pore sensing (NPS)^{19,20,21,22,23,24}, which involves using a four-point probe measurement to measure the modulated current pulse produced by a cell transiting a microfluidic channel that has been segmented by wider regions, referred to as "nodes". The modulated current pulse is a result of the cell partially blocking the flow of current in the segments (i.e., "pores") and nodes, with more current blocked in the former than in the latter. In mechano-NPS, one segment, the "contraction channel", is narrower than a cell diameter; consequently, a cell must deform to transit the entire channel (Figure 1B). Cell diameter can be determined by the magnitude of the subpulse produced when the cell transits the node-pores prior to the contraction channel (Figures 1B,C). Here, |ΔI_np|, the current drop when the cell is in the pore, is proportional to the volume ratio of the cell to the pore, V_cell/V_pore^2,8,19. Cell stiffness can be determined by ΔT_c, the duration of the dramatically larger subpulse produced when the cell transits the contraction channel (Figures 1B,C). A stiffer cell will take longer to transit the channel than a softer one^2,8. Finally, cell "recovery", the cell's ability to return to its original size and shape post deformation, can be determined by the series of subpulses produced as the cell transits the node-pores after the contraction channel (Figures 1B,C). The recovery time, ΔT_r, is the time it takes for the current subpulses to return to the magnitude of the previous subpulses, prior to the cell being squeezed. Overall, the modulated current pulses produced as a cell transits the microfluidic channel are recorded and analyzed to extract the relevant single-cell mechanical parameters (Figure 1D)^2,8.

The reproducibility and ease of use of this electronics-based microfluidic platform have been previously demonstrated²⁵. Additionally, the platform presents a low barrier to entry for single-cell mechanophenotyping. Standard soft lithography is employed to fabricate microfluidic devices. The measurement hardware consists of inexpensive components, including a simple printed circuit board (PCB), power supply, preamplifier, data acquisition board (DAQ), and computer. Finally, user-friendly code is available for data acquisition and analysis, enabling straightforward implementation. This mechanophenotyping technique can distinguish populations of non-malignant and malignant breast and lung epithelial cell lines, discriminate between sublineages in primary human mammary epithelial cells, and characterize the effects of cytoskeletal perturbations and other pharmacological agents^2,8. Overall, this platform is an effective approach for the mechanophenotyping of single cells.

1. Design device geometry

1. Choose the width of the sizing and recovery segments so that it is wider than the diameter of the largest cells to be measured but also maintains a sufficient signal-to-noise ratio (SNR). See Supplementary Table 2 for examples of different sizing and recovery segment widths for various cell lines.
2. Choose the contraction segment width to apply a 30%-40% strain to the average size of the cells that are to undergo mechanophenotyping. Strain is defined as , where d is cell diameter and w_c is the contraction channel width^2,8. See Supplementary Table 2 for different contraction segment widths for various cell lines.
   NOTE: If one wishes to compare cell types or conditions with substantially different diameters, separate device designs should be used with contraction segment widths specific to each cell type/condition.
3. Design a reference device for each unique device geometry. This is necessary for determining D_e, the effective diameter of the sizing pore segment of the microfluidic channel.
   NOTE: The reference device uses the same geometry as the primary device. The only modification is that the contraction segment should be equal in width to the sizing pore segment to allow for calibration with polystyrene beads of a known size. Widening the contraction prevents the polystyrene beads from clogging the contraction channel during calibration. The calibration process is further described in steps 4.1 and 5.3.1. Calibration can also be achieved using a commercially available cell counter, in which case, no reference device is needed. This process is described in step 4.2.
4. Choose the channel height such that the largest cells of interest can fully elongate without restriction within the contraction segment². Ensure that the channel height is larger than h_min (this assumes the cell is spherical pre-deformation, and that isometric deformation occurs along the channel length and height during deformation).
   NOTE: Given the magnitude of a current subpulse, , the larger the h_min is, the lower the overall SNR will be.
5. Design and create a photomask using computer-aided design software with the chosen channel widths. An example file is provided in Supplementary File 1. Scale the microfluidic mask design by 1.5% to account for polydimethylsiloxane (PDMS) shrinkage after peeling from the negative master.
   NOTE: An array of devices can be included on a single mask as long as the overall array does not exceed the size of the wafer (Supplementary Figure 1A).
6. Design and create a photomask with electrodes that will be used to perform a four-point probe measurement of the microfluidic device current (Figure 1D). An example file is provided in Supplementary File 1.
   NOTE: An array of electrodes can be included on a single mask as long as the array does not exceed the size of the glass slide (Supplementary Figure 1B).

2. Fabricate devices (Figure 2)

1. Prepare electrode patterns on a glass substrate.
   1. Spin coat, pattern, and process a positive photoresist onto a plain glass slide according to the product data sheet. An example of this procedure is outlined in Supplementary File 2.
   2. Perform metal deposition, lift-off, and gold etching.
      1. Perform thin film deposition of 75 Å Ti, 250 Å Pt, and 250 Å Au onto the slide. An example of this procedure using electron-gun evaporation is outlined in Supplementary File 3.
      2. Immerse the slide in acetone for 15 min to perform a lift-off of excess metal.
      3. In a fume hood, use a disposable pipette to drop-cast gold etchant onto the region of electrodes that will be exposed to the microfluidic channel, as shown in Supplementary Figure 2. Be cautious to avoid dropping etchant elsewhere on the slide.
         CAUTION: Gold etchant can cause skin and eye irritation. Do not breathe vapors, and do not ingest. Handle with care, wear appropriate personal protective equipment (PPE), and discard waste according to local disposal regulations.
      4. Rinse the slide with deionized (DI) water and dry it with dry nitrogen (N₂).
   3. If multiple electrodes are printed on the same glass slide, dice the slide into individual chips.
      1. Use a glass cutting tool to score the slide along the patterned electrode boundaries.
      2. Break the glass along the score to partition the slide into individual chips.
   4. Visually inspect the electrodes under a microscope. Ensure that individual electrodes are not electrically open or that electrodes are not shorted together.
2. Fabricate a negative master mold for channels.
   1. Spin coat, pattern, and process an SU-8 epoxy resist onto a polished silicon wafer according to the product data sheet. An example of this procedure is outlined in Supplementary File 2.
   2. Measure feature heights using a profilometer and visually inspect the features under a microscope (Supplementary Figure 3). Ensure that the desired geometries are well-defined.
3. Mold PDMS channels with soft lithography.
   1. Prepare PDMS by weighing an elastomer and a crosslinker at a 10:1 mass ratio in a disposable cup.
      NOTE: For a wafer with a 3 in diameter, 30 g of PDMS is sufficient.
   2. Mix the PDMS vigorously for 30 s with a disposable fork, until the PDMS is opaque with bubbles.
   3. De-gas the PDMS in a vacuum chamber for approximately 30-90 min, or until the PDMS is transparent with no visible bubbles.
   4. Place the wafer with the SU-8 master mold into a disposable Petri dish and pour PDMS over the center of the wafer.
   5. Place the Petri dish containing the PDMS and wafer in a vacuum chamber and de-gas for approximately 30 min, or until no bubbles remain in the PDMS.
   6. Bake the PDMS at 80 °C for 2 h in an oven or on a hot plate.
   7. With a sharp blade, cut and remove the PDMS from the SU-8 negative master.
   8. Dice the molded PDMS slab into individual molds using a sharp blade
   9. Core the inlet and outlet access holes using a disposable biopsy punch. For best results, use a new punch for each PDMS slab. A sharper punch produces smooth-edged holes, minimizing particulates that could obstruct the contraction channel.
      NOTE: The diameter of the access holes should be slightly less than the outer diameter of the tubing. For example, if using polytetrafluoroethylene (PTFE) tubing with an outer diameter of 1/32 in, a 1.5 mm hole should be punched.
4. Bond a glass/electrode substrate to the PDMS channels.
   1. Clean the electrode glass slides with methanol (≥99.8%). Dry with dry N₂.
   2. Clean the PDMS device with scotch tape, followed by a rinse with isopropyl alcohol (IPA) and deionized water (DI; 18 MΩ/cm²). Dry with dry N₂. Then, clean with scotch tape once more.
   3. Place the glass substrate with prefabricated electrodes and the prepared PDMS mold (feature side up) into a plasma cleaner.
   4. Expose both to oxygen plasma for 2 min (100-300 mTorr, 30 W).
   5. Align and place the PDMS mold with the feature side face-down onto the glass substrate with prefabricated electrodes.
      NOTE: Bonding is instantaneous once the plasma-treated PDMS and glass come into contact; consequently, further alignment modifications will not be possible. To facilitate alignment, 20 µL of a 2:1 dilution of methanol in DI water may be pipetted onto the plasma-treated glass surface. The methanol solution acts as a physical barrier between the treated glass and PDMS, allowing for alignment adjustments. If using methanol, bake the aligned and mated device at 50 °C for 2 h to evaporate the solution and complete the bonding process.
   6. Visually inspect the bonded device under a microscope. Ensure that the electrodes and channel geometries are properly aligned.

3. Measure cells (Figure 1D)

1. Prepare the pressure source, PCB, benchtop hardware, and data acquisition software.
   1. Connect the microfluidic device to the PCB using the clamp. An example of the PCB is provided in Supplementary File 4 (GERBER files) and Supplementary File 5 (schematic, board, and PCB parts list files).
      1. Align the clamp's spring-loaded pins with the electrode contact pads on the microfluidic device and align the clamp's header pins with the holes on the PCB.
      2. Firmly insert the clamp's header pins into the PCB holes, making sure the spring-loaded pins stay aligned with the electrode contact pads.
   2. Set up and connect the electronic hardware.
      1. Connect two of the power supply's output ports to the PCB's supply voltage port with a double banana plug-to-Bayonet Neill-Concelman (BNC) female adapter and a BNC cable.
      2. Turn on the power supply. Set the output connected to the BNC's inner conductor to +15 V and set the other output to -15 V. Enable both outputs to power the circuit.
      3. Connect the third of the power supply's output ports to the input voltage port of the PCB with a BNC cable. Set the output to the desired applied voltage, but do not enable it until starting the experiment.
      4. Connect the PCB's output current port to the input of the current preamplifier with a BNC cable.
      5. Connect the output of the current preamplifier to one analog input on the BNC terminal block of the data acquisition system with a BNC cable. Optionally, connect an analog low-pass filter in line with the BNC cable to filter out high-frequency interference.
         NOTE: To improve the SNR, the PCB and device may be housed within a thick metal enclosure. All BNC cables and fluidic tubing can be routed through holes drilled into the enclosure.
   3. Install and set up the required software on the personal computer (PC)
      1. Power on and connect the pressure controller to the PC. Install any required pressure controller software as per the manufacturer's instructions.
      2. Install MATLAB and the Data Acquisition Toolbox on the PC. Ensure the required drivers for the data acquisition system are installed so that the MATLAB Data Acquisition Toolbox interface can detect it.
      3. Download the included data acquisition script, "NPS.m", from https://github.com/sohnlab/node-pore-sensing-public.
   4. Open and configure the data acquisition script.
      1. Set the correct values to initialize the data acquisition session, which includes the Vendor ID, the DAQ's Device ID, and the analog input channel number (lines 34-36 in the included script).
         NOTE: The Device ID can be found using the function "daq.getDevices" or "daqlist".
      2. Set the desired sample rate for the acquisition (line 23 in the included script). For optimal results, it should be set to at least 10 kHz.
2. Prepare the cell suspension.
   1. Prepare a solution of 2% fetal bovine serum (FBS) in 1x phosphate buffered saline (PBS), and filter with a 0.22 µm filter.
   2. Culture and prepare the cells according to the appropriate cell-culture protocol of the cell line of choice. Suspend the cells in the prepared solution of 2% FBS in 1x PBS at a concentration of 1-5 x 10⁵ cells/mL. Keep the cells on ice for the duration of the experiments.
3. Measure the physical properties of the cells.
   1. Load the cell sample into the tubing and connect it to the device inlet.
      1. Cut 30 cm of PTFE tubing with a razor blade or sharp knife.
      2. Attach one end of the tubing to a luer lock syringe. Use the syringe to draw up the cell sample into the other end of the tubing.
      3. Carefully insert the tubing into the inlet of the device.
      4. Connect the opposite end of the tubing to the microfluidic pressure controller.
         NOTE: A filter can be added between the microfluidic pressure controller and the tubing to prevent liquid backflow into the pressure controller.
   2. Run the experiment.
      1. Set the desired constant driving pressure on the pressure controller software and allow the sample to fill the device.
         NOTE: The pressure is typically 2-21 kPa. The flow speed must be slow enough to allow for clearly defined pulses but fast enough to allow for adequate throughput.
         1. If bubbles form in the microfluidic channels, use dead-end filling: plug the device outlet and apply a low pressure to the inlet to force air out through the gas-permeable PDMS. Leaving bubbles in the channel will lead to an unstable current baseline and prevent accurate measurements.
         2. If debris clogs the microfluidic channel, dislodge it by lightly pressing on the top of the PDMS device while applying the driving pressure, "pulsing" a higher pressure by toggling the pressure on and off, or removing the tubing and reinserting it. If the debris remains, it may be necessary to switch to a new device.
      2. Set the desired voltage by rotating the Voltage knob on the power supply and enable the voltage by pressing the On button.
         NOTE: Voltage is typically 1-5 V. Choose the lowest voltage necessary for an adequate SNR. The same voltage should be used across all conditions to be compared.
      3. Turn on the current preamplifier and set the sensitivity (A/V) as low as possible; alternatively, set the gain (V/A) as high as possible without overloading the preamplifier or exceeding the maximum analog input voltage of the DAQ. In this study, the sensitivity was set to 10^-7 A/V.
         NOTE: The proper sensitivity/gain value will depend on both the applied voltage as well as the baseline resistance of the microfluidic channel.
      4. Press the green Run button in the MATLAB ribbon menu to begin the data acquisition script NPS.m and start sampling and saving the data.
      5. To end the experiment, press the Stop button in the lower left corner of the figure window to stop the data acquisition script. Disable the power supply output by pressing the On button. Set the pressure source to zero pressure in the pressure controller software.
      6. At this point, the experiment can be paused to do one or more of the following:
         1. Replace the current device with a new one.
         2. Reload the tubing with more cell samples.
            NOTE: To avoid sample cross-contamination, use new devices to measure cells of different types or conditions.
         3. Unclamp the device from the PCB and examine the channel's condition under a microscope. To restart the experiment using the same device, care must be taken not to introduce air bubbles. It may be necessary to apply gentle pressure to the syringe plunger to keep the cell sample at the very end of the tubing while inserting it into the device inlet.

4. Calibrate the microfluidic device

1. Option 1: Measure the polystyrene beads in reference devices.
   1. Choose a polystyrene bead size that is smaller than the sizing channel.
   2. Add 1.5% Tween and polystyrene beads to the filtered PBS and FBS solution used during the cell experiments, at a concentration of 1-3 x 10⁵ beads/mL.
   3. Proceed with the experiment as outlined in section 3, using the reference device described in step 1.3, and apply the same voltage used during experimentation. Use the average magnitude of the current drop produced as beads transit the sizing pores and the known diameter of the beads to calculate D_e, as described in section 5.
2. Option 2: Independently measure the cell size with a separate measurement device.
   1. Instead of following the protocol in step 4.1, use a commercially available cell size measurement instrument to measure the average size of cells in the sample. In this case, no reference device is needed. Use the average current drop produced as cells transit the sizing pore and the measured average cell diameter to calculate D_e as described in section 5.

5. Analyze data to extract cell phenotypes

NOTE: Data processing can be performed using the MATLAB command-line interface program file mNPS_procJOVE.m at https://github.com/sohnlab/NPS-analysis-JOVE. See Supplementary File 6 for more instructions.

1. Preprocess the data (Figure 3A).
   1. Compute the measured electrical current by applying the gain value used in the current-to-voltage preamplifier to the raw data acquired by the DAQ.
   2. Remove high-frequency noise by applying a rectangular smoothing function and/or a low-pass filter to the raw current measurement. Then, resample the filtered data to a lower sample rate. Also, compute the corresponding timestamp data at this lower sample rate.
   3. Compute a fitted baseline current signal by applying a method such as asymmetric least-squares smoothing²⁶.
   4. Compute the approximate first derivative (difference signal) of the preprocessed current data by taking the difference between subsequent data points.
2. Identify cell events and extract subpulse data (Figure 3B).
   1. Search for candidate cell events by examining the preprocessed data. Reject cell events that overlap with other cell events (i.e., coincidence events) (Supplementary Figure 4), exhibit a poor baseline fit, or have an unexpected or erroneous pulse shape (e.g., where a clog may have been present in the channel).
   2. Extract subpulse data for each cell event.
      1. Each node-pore segment will appear as a corresponding subpulse within the overall signal pulse (Figures 1B, C). Identify the start of each subpulse by computing the timepoint when the difference signal reaches a local minimum value. Identify the end of each subpulse by computing the timepoint when the difference signal reaches a local maximum value.
      2. Determine the width of each subpulse as the elapsed time between the start and end time points. Determine the amplitude of each subpulse by computing the mean of the difference between the measured current and the baseline current for all data points between the start and end time points.
3. Determine the cell mechanophenotype for each cell event based on subpulse data.
   1. Determine the cell diameter d based on the equation defined by Deblois and Bean²⁴:
      
      where ΔI/I is the mean ratio of subpulse amplitude to baseline current in the sizing subpulses, D_e is the effective diameter of the channel (measured in step 4), and L is the total length of the node-pore channel.
      1. D_e is determined by calculating the average ΔI/I produced by a set of particles of a known diameter (either cells or beads, see step 4), using that known diameter as d, and solving Eq. 1 for D_e.
   2. Quantify the cell's resistance to deformation.
      1. Determine the fluid velocity U_flow by calculating the mean cell velocity in the sizing subpulses, using the known segment lengths and measured duration of each subpulse.
      2. Determine the whole-cell deformability index (wCDI), defined by Kim et al.² as:
         
         where L_c is the length of the contraction segment, h_channel is the channel height, and ΔT_c is the duration of the contraction subpulse.
   3. Identify the cell's recovery time from deformation, defined as the first recovery subpulse with an amplitude within 8% of the mean amplitude from the sizing subpulse².
   4. Calculate the cell's transverse deformation within the contraction segment.
      1. Calculate the effective diameter of the contraction segment (D_e,c) as defined by Kim et al.²: , where w_c is the width of the contraction segment and w_np is the width of all other segments.
      2. Calculate the equivalent spherical diameter d_c of the cell within the contraction by again using the equation defined by Deblois and Bean²⁴:
         
         where ΔI_c/I_c is the ratio of subpulse amplitude to baseline current in the contraction subpulse and L_c is the length of the contraction segment.
      3. Calculate the cell's elongation length L_deform as described by Kim et al.²:
         
      4. Finally, compute the cell's transverse deformation δ_deform, which is defined by Kim et al.² to be .

The mechanophenotyping platform presented here is a simple and versatile approach for measuring the biophysical properties of single cells with moderate throughput. Cells are flowed through the microfluidic channel (Figure 1A) using constant pressure-driven flow. As the cells transit, the length of the microfluidic channel and the current pulses produced are recorded using the data acquisition hardware. The acquired signal (Figure 1B,C) is then processed using custom software on MATLAB to extract the relevant single-cell mechanical properties. Figure 1D presents a graphical overview of the experimental protocol.

To fabricate devices, a negative master mold is initially created and used to cast PDMS (Figure 2). Successful master molds, shown in Supplementary Figure 3, contain smooth, vertical sidewalls and no defects in the microfluidic channel (Figure 4Ai). Care should be taken in fabricating this initial master mold because, in poorly fabricated wafers (Supplementary Figure 3), any defects will transfer to all subsequent PDMS casts (Figure 4Aii), making them unusable. Successful PDMS casts are then plasma-bonded to glass slides with patterned electrodes (Figure 1A).

For experiments, a device's electrode pads are connected to the PCB (Figure 1D) to enable current measurement. Tubing, containing a cell sample, is then inserted into the device inlet and connected to a microfluidic flow controller, enabling a pressure-driven flow of cells through the microfluidic channel. Importantly, a live readout of the current measurement is displayed during an experiment. This enables users to ensure their device is operating as intended. Successful cell transit events consist of easily distinguishable subpulses (Figure 4Bi). Complications, such as clogging, may occur during experimentation and can be identified by the live readout of events with abnormal pulse shapes (Figure 4Bii).

For data analysis, the critical signal parameters that need to be extracted for each cell transit event are detailed in Figure 1B and described in step 5.2.2. The raw signal should have a sufficient SNR to filter out the noise and extract the significant components (Figure 3A). Critically, the current signal rise from each node should be robust enough such that subpulses can be easily identified from the difference signal ∂I/∂t (Figure 3B).

The measured wCDI and recovery times can be used to make direct comparisons between cells or conditions. Specifically, wCDI is a relative indicator of cell stiffness that is inversely proportional to the elastic modulus (Supplementary Figure 5); thus, a greater value of wCDI corresponds to a softer mechanical phenotype. For example, in Figure 5A, malignant MCF-7 cells have a greater wCDI distribution than non-malignant MCF-10A cells, indicating that the malignant MCF-7 cells are softer than their non-malignant MCF-10A counterparts. This is consistent with numerous empirical studies that have shown cancer cells to be softer than their non-malignant counterparts²⁷. Likewise, in Figure 5B, MCF-10A and MCF-7 cells treated with latrunculin show an increase in wCDI. Latrunculin is a potent pharmacological agent that disrupts the actin cytoskeleton²⁸. Consequently, it is consistent to observe a greater wCDI, and thus a softer phenotype, in cells treated with this drug. Finally, in Figure 5C, wCDI is compared between two human mammary epithelial cell sublineages, luminal epithelial (LEP) and myoepithelial (MEP). In this comparison, there is once again a distinct wCDI distribution differentiating the two primary cell types, indicating that LEP cells are softer than MEP cells. Beyond wCDI, recovery times can also be quantified to provide a relative indicator of cell viscosity. A longer recovery time indicates a more viscous phenotype. Figure 5D presents the recovery times, binned into three distinct categories, for each of the conditions in Figures 5A-C. Untreated MCF-10As and MCF-7s have a greater proportion of cells that recover instantly (ΔT_r = 0), indicating a lower viscosity than their latrunculin treated counterpart.

wCDI and recovery time are relative metrics of single-cell elasticity and viscosity. Therefore, the method presented here is best suited to characterizing the differential response between multiple samples of interest. In its current form, the protocol presented here does not provide absolute quantifications of defined mechanical parameters, such as Young's modulus.

Figure 1: Overview of the mechanophenotyping platform. (A) Image of the microfluidic device. (B) Channel geometry, with a representative current pulse from a single cell transit event. ΔI_np represents the current drop from the cell entering the pore, and ΔI_c represents the current drop from the cell entering the contraction. ΔT_p represents the time needed for the cell to transit the pore, ΔT_c represents the time needed for the cell to transit the contraction channel, and ΔT_r represents the time needed for the cell to recover. (C) Real current pulse from a cell transit event. (D) Experimental workflow: (1) cells are suspended in PBS and (2) driven through the microfluidic device with constant pressure. As the cells transit the microfluidic channel, (3) current pulses are measured using data acquisition hardware. (4) The current pulses are analyzed to extract multiple single-cell mechanical properties. Please click here to view a larger version of this figure.

Figure 2: Overview of Device fabrication. (A) Negative master molds are (1) fabricated using photolithography. (2) PDMS is then cast onto the negative master molds. (3) The molded devices are diced, with inlet and outlet holes punched. (B) Simultaneously, glass slides are (1) patterned to fabricate metal electrodes. (2) E-beam evaporation is used to deposit metal onto the slide, followed by a (3) lift-off process to remove excess metal, leaving behind the desired metal electrodes. (C) The PDMS device and glass with metal electrodes are then bonded together using oxygen plasma to complete the fabrication process. Please click here to view a larger version of this figure.

Figure 3: Data processing and analysis. (A) Raw current data (left) is preprocessed (right) by applying a rectangular smoothing function and low-pass filter, followed by resampling to a lower sample rate. The baseline current signal is subsequently fitted with asymmetric least-squares smoothing²⁶. (B) The timepoints at the start and end of each subpulse are identified as local minimums and maximums, respectively, in the difference signal ∂I/∂t (left). For each subpulse, the width Δt is determined by the elapsed time between the start and end, and the amplitude ΔI is determined by the mean difference between the measured current and the baseline current. The extracted subpulse data can be represented as a rectangularized signal (right); each subpulse corresponds to one segment in the device geometry. Please click here to view a larger version of this figure.

Figure 4: Examples of successful and flawed results during fabrication and experimentation. (A) Images of PDMS casts of contraction segments taken from two different negative-master molds. (i) is an example of a well-fabricated channel with smooth sidewalls, well-defined geometry, and no noticeable defects. (ii) is an example of a poorly fabricated channel with significant defects in the contraction channel (outlined in red rectangles), which would either fully or partially obstruct particle flow (scale bars = 150 µm). (B) Examples of filtered current pulses generated by "NPS.m" during data acquisition. (i)Example of a successful cell event, where a cell transits the channel as intended. (ii)Example of current pulses produced when the device is "clogged". In this case, debris obstructs cell flow in the channel's second pore. This leads to cell events that appear "cut off" (indicated by the red line) midway through the second sizing pulse. The decreases in the current (indicated by the blue lines) following the "cut off" are caused by particles (debris or cells) that are building up around the blockage and therefore are blocking a larger portion of current flow. A sharp downward spike in the current (indicated by the green rectangle) reflects a particle that is able to transit around the blockage and, therefore, only temporarily block a larger portion of the current. Please click here to view a larger version of this figure.

Figure 5: Examples of wCDI and cell recovery between various conditions. (A) wCDI distribution between two breast epithelial cell lines, non-malignant MCF-10A, and malignant MCF-7. (B) wCDI of MCF-10A or MCF-7, where each cell type was untreated, treated with Lat-A, and treated with Lat-B. (C) wCDI distribution between two primary human mammary epithelial sublineages: myoepithelial (MEP) and luminal epithelial (LEP) cells. (D) Recovery times corresponding to the four conditions among A, B, and C. Recovery times were binned for ease of viewing. All conditions had a sample size of n = 99 cells. This figure is reproduced with permission from Kim et al.². Please click here to view a larger version of this figure.

Supplementary Figure 1: Examples of device arrays for both the microfluidic channels and electrodes. Both images, represented as the transparency masks, should be designed with white (representing transparent) in the regions where the photoresist will be exposed to UV and black in the regions where it will be blocked from UV exposure. (A) An array of microfluidic channels, with two parallel channels per "device" (rectangle), arranged on a 3 in Si wafer. The device on the far right is labeled as "ref" and is designed, as described in step 1.3, for use in calibration. (B) An array of electrodes, with two electrodes per device, such that both channels in a device from (A) will align over each electrode design from (B). This array was arranged for a 2 in x 3 in glass slide. Please click here to download this File.

Supplementary Figure 2: Schematics illustrating regions of the electrode design and how to etch away gold correctly (step 2.1.2.3). The top layer of gold must be removed from the measurement electrodes, or biofouling and electrolysis will occur during measurement. However, gold should remain on the contact pads, because the soft gold provides a superior electrical connection with the pins of the pogo pin connector. (A) Schematic showing how the microfluidic channel (in blue) intersects with the patterned metal (black). As indicated, the metal perpendicular to the microfluidic channel and directly under it are the measurement electrodes, where gold must be etched, while the large metal rectangles to the side of the channel are the contact pads, where gold must remain. (B) Schematic showing where gold etchant should be applied to the patterned metal, as well as where it should not. The green region indicates where etching is necessary, the yellow region indicates where etching may occur and will not impact the effectiveness of the device, and the red region indicates where gold must not be etched. (C) Schematic showing a typical, successfully etched device. Yellow indicates regions where gold is still present, and gray indicates regions where the platinum layer has been exposed. Please click here to download this File.

Supplementary Figure 3: Images of successfully and poorly fabricated contraction channels. (A) Images of contraction channels on negative master molds from two individual Si wafers. (i) An example of a well-fabricated master mold that would produce an ideal PDMS channel such as that shown in Figure 4Ai. (ii) An example of a poorly fabricated master mold, which was used to create the PDMS channel shown in Figure 4Aii. The regions outlined in red rectangles correspond to the defects indicated in Figure 4Aii, demonstrating the transference of defects from the master mold to the PDMS microchannels (scale bars = 150 µm). (B) Images of the cross-section of PDMS contraction channels, showing the height and width of different molds. These cross-sections were acquired by slicing the PDMS contraction channel perpendicular to the direction of flow. (i) An example of a well-fabricated PDMS mold created using the wafer shown in (Ai), demonstrating smooth vertical sidewalls. (ii) An example of a poorly fabricated PDMS mold created using the wafer shown in (Aii) demonstrating slanted sidewalls (scale bars = 20 µm). Please click here to download this File.

Supplementary Figure 4: Example of a coincident cell event (produced when more than one cell is transiting the channel at once). The signal was processed according to steps 5.1.1-5.1.3 in section 5 of the protocol. Please click here to download this File.

Supplementary Figure 5: Relation between wCDI and the elastic modulus^{20,29,30,31,32,33,34,35}. (A) Comparison of Jurkat, MCF7, and MCF10A cells measured by this technique versus micropipette aspiration. wCDI is inversely proportional to cortical tension. (B) Comparison of MCF7 and MCF10A cells measured by this technique versus published AFM data. (C) Comparison of A549 and BEAS-2B cells measured by this technique versus published AFM data. Over multiple cell types, wCDI is inversely proportional to elastic modulus. This figure has been reproduced with permission from Kim et al.². Please click here to download this File.

Supplementary Table 1: Examples of single-cell mechanophenotyping techniques and how they compare to mechano-NPS. Techniques are listed in order of increasing throughput^12,2,36,37. Mechanical information refers to whether the device can measure both the elastic and viscous mechanical properties of each cell. Please click here to download this Table.

Supplementary Table 2: Example of sizing, contraction, and recovery segment widths for different cell lines. MEP and LEP refer to two lineages of primary human mammary epithelial cells, where MEP refers to myoepithelial cells and LEP refers to luminal epithelial cells^2,8. Please click here to download this Table.

Supplementary File 1: AutoCAD design example. Please click here to download this File.

Supplementary File 2: Example protocol for photolithography processing. Two example protocols are outlined for patterning and processing photoresist or SU-8 epoxy. The first describes the application of positive photoresist on a glass slide to act as a sacrificial layer for electrode fabrication. The second describes the application of SU-8 epoxy on a silicon wafer to create relief structures for molding PDMS microfluidic channels. These protocols were adapted from MF-321 and SU8 3000 data sheets from the manufacturer. Please click here to download this File.

Supplementary File 3: Example protocol for thin film deposition of 75 Å Ti, 250 Å Pt, and 250 Å Au using electron beam evaporation. An example protocol for performing thin film deposition of 75 Å Ti, 250 Å Pt, and 250 Å Au using an electron beam evaporator is outlined. Please click here to download this File.

Supplementary File 4: GERBER files Please click here to download this File.

Supplementary File 5: Schematic, board, and PCB parts list files Please click here to download this File.

Supplementary File 6: MATLAB command-line interface. This file includes detailed instructions for data processing using the MATLAB command-line interface²⁶. Please click here to download this File.

Measuring the mechanical properties of single cells using this mechanophenotyping technique consists of three stages: device fabrication, data acquisition, and data analysis. Within each stage, there are notable aspects that may significantly impact the experimental results. During device fabrication, consistent channel geometries and device-to-device uniformity are essential for accurate and repeatable results. Specifically, the sidewalls of each device should be relatively smooth (Figure 4Ai), and the channel heights across replicate devices should be comparable. Any devices with defects that partially obstruct cell flow, particularly in the contraction channel (Figure 4Aii) should be discarded. Using devices with discernable defects in the channel geometry will result in inaccurate and unreproducible results. During data acquisition, it is vital that the user can recognize the presence of a clog in the channel during data collection. An example script, "NPS.m" (available at https://github.com/sohnlab/node-pore-sensing-public), displays current measurements, enabling the user to monitor the channel condition and cell transit events in real time. Pulses characteristic of a clog are shown in Figure 4B. A clog within the contraction channel that still allows some cells to flow is particularly important to address, as cells will experience increased strain at the location of the obstruction, resulting in inaccurate wCDIs. Finally, during data analysis, users should be diligent in choosing accurate thresholds as inputs for the analysis algorithm. If the thresholds are set too high, the program will not identify events produced by smaller cells, skewing results and misrepresenting the measured cell population.

Beyond these critical stages, there are additional aspects of the protocol that may require adjustment when studying different cell types or conditions. For example, applying this technique to samples that are significantly smaller than those previously studied necessitates narrower contraction channels. Fabricating narrower channels may require more costly fabrication methodologies, such as electron beam lithography³⁸. Additionally, because ΔI/I ~ ΔV_cell/ΔV_contraction (where V_cell and V_contraction are the volumes of the deformed cell and channel, respectively), narrower channels result in greater baseline resistances and decreased SNR¹⁹. Finally, narrow channels may increase the risk of clogging, making experimental execution more challenging. This particular issue could potentially be mitigated by utilizing more inlet filters.

One limitation of this technique is the static contraction channel, which can only apply a single average strain to a population of cells. In order to compare the deformability of cells with differing sizes, or to investigate a single cell population at different applied strains, multiple devices with varying contraction channel widths must be employed. The platform is also limited by its PDMS walls, which restrict the range of stiffnesses it can measure. For example, some very rigid plant cells are stiffer than the PDMS walls that would be expected to deform them^39,40. Furthermore, the pressure required to push such stiff particles through the contraction channel may overcome the strength of the bond between the PDMS and glass slide, leading to delamination. However, one could fabricate microfluidic channels into stiffer materials such as glass or silicon, to thus overcome these limitations. Despite these minor constraints, this platform remains ideal for moderate-throughput mechanical measurement of cells. In comparison to other methodologies, such as optical stretchers or AFM, this technique enables higher throughput. Compared to other moderate- to high-throughput microfluidic technologies, such as RT-DC, this technique is capable of investigating more biophysical properties by measuring both the viscous and elastic properties of single cells. Finally, the simple experimental setup, utilizing inexpensive electronics as opposed to complex optics, makes this technique a highly accessible technology.

Overall, this mechanophenotyping technique holds immense promise as a tool for numerous potential studies and applications. It has previously been applied to a diverse set of cell types, including primary samples, and has shown its effectiveness at measuring the impact of cytoskeletal and nuclear components on cellular viscoelastic properties^2,8. Furthermore, because cells remain viable after screening with this platform², researchers have the opportunity to perform downstream analysis. This platform is also ideal for coupling with upstream microfluidic cell sorting, lending itself to applications such as measuring circulating tumor cells. Looking forward, this platform remains a competitive and versatile tool for multivariable mechanical measurements of single-cells, with applications in understanding cell behavior⁴¹, determining disease progression⁴², and monitoring drug response⁴³. Beyond fundamental scientific research, this technique also holds great promise as a clinical tool, specifically a low-cost, benchtop platform for rapid diagnostic screening. Furthermore, given the low power requirements and minimal need for sample preparation prior to measurement, this technique is particularly well suited for low-resource environments, where accessible diagnostic instruments are sorely needed⁴⁴.