Presented here is a method to mechanically phenotype single cells using an electronics-based microfluidic platform called mechano-node-pore sensing (mechano-NPS). This platform maintains moderate throughput of 1-10 cells/s while measuring both the elastic and viscous biophysical properties of cells.
Cellular mechanical properties are involved in a wide variety of biological processes and diseases, ranging from stem cell differentiation to cancer metastasis. Conventional methods for measuring these properties, such as atomic force microscopy (AFM) and micropipette aspiration (MA), capture rich information, reflecting a cell’s full viscoelastic response; however, these methods are limited by very low throughput. High-throughput approaches, such as real-time deformability cytometry (RT-DC), can only measure limited mechanical information, as they are often restricted to single-parameter readouts that only reflect a cell’s elastic properties. In contrast to these methods, mechano-node-pore sensing (mechano-NPS) is a flexible, label-free microfluidic platform that bridges the gap in achieving multi-parameter viscoelastic measurements of a cell with moderate throughput. A direct current (DC) measurement is used to monitor cells as they transit a microfluidic channel, tracking their size and velocity before, during, and after they are forced through a narrow constriction. This information (i.e., size and velocity) is used to quantify each cell’s transverse deformation, resistance to deformation, and recovery from deformation. In general, this electronics-based microfluidic platform provides multiple viscoelastic cell properties, and thus a more complete picture of a cell’s mechanical state. Because it requires minimal sample preparation, utilizes a straightforward electronic measurement (in contrast to a high-speed camera), and takes advantage of standard soft lithography fabrication, the implementation of this platform is simple, accessible, and adaptable to downstream analysis. This platform’s flexibility, utility, and sensitivity have provided unique mechanical information on a diverse range of cells, with the potential for many more applications in basic science and clinical diagnostics.
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 composition2,3,4, 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 approaches5,6,7. 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 assessment12,13. These techniques employ narrow constriction channels14,15, 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, |ΔInp|, the current drop when the cell is in the pore, is proportional to the volume ratio of the cell to the pore, Vcell/Vpore2,8,19. Cell stiffness can be determined by ΔTc, 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 one2,8. 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, ΔTr, 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 agents2,8. Overall, this platform is an effective approach for the mechanophenotyping of single cells.
1. Design device geometry
2. Fabricate devices (Figure 2)
3. Measure cells (Figure 1D)
4. Calibrate the microfluidic device
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.
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 (ΔTr = 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. ΔInp represents the current drop from the cell entering the pore, and ΔIc represents the current drop from the cell entering the contraction. ΔTp represents the time needed for the cell to transit the pore, ΔTc represents the time needed for the cell to transit the contraction channel, and ΔTr 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 modulus20,29,30,31,32,33,34,35. (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 throughput12,2,36,37. 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 cells2,8. 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 ~ ΔVcell/ΔVcontraction (where Vcell and Vcontraction 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 them39,40. 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 properties2,8. 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.
The authors have nothing to disclose.
This research was supported by grants from NIBIB 1R01EB024989-01 and NCI 1R01CA190843-01. A. L. and R. R. were supported by an H2H8 Association Graduate Research Fellowship. K. L. C. was supported by a National Science Foundation Graduate Research Fellowship and a Siebel Scholar Fellowship.
Acetone | J.T. Baker | 5356-05 | Purity (GC) ≥ 99.5% (https://us.vwr.com/store/product/6057739/acetone-99-5-vlsi-j-t-baker) |
Aluminum Foil | n/a | n/a | |
Analog Low-Pass Filter | ThorLabs | EF504 | ≤240 kHz Passband, Coaxial BNC Feedthrough (https://www.thorlabs.com/thorproduct.cfm?partnumber=EF504#ad-image-0) |
Biopsy Punch | Integra Miltex | 33-31AA-P/25 | 1mm, Disposable, with Plunger (https://mms.mckesson.com/product/573313/Miltex-33-31AA-P25) |
Blade | n/a | n/a | |
BNC Cable | Pomona Electronics | 2249-C-12 | https://www.digikey.com/en/products/detail/pomona-electronics/2249-C-12/603323?utm_adgroup=Coaxial%20Cables%20%28RF%29&utm_source=google&utm_ medium=cpc&utm_campaign= Shopping_Product_Cable%20Assemblies_NEW&utm_term= &utm_content=Coaxial%20Cables%20%28RF%29&gclid=Cj0KCQjwlK-WBhDjARIsAO2sErQqnVJ pj5OXVObuTI8ZUf1ZeIn7zvzGnx mCWdePrG6SdEJMF3X6ubUaAs w-EALw_wcB |
Cleanroom Polyester Swab | Thermo Fisher Scientific | 18383 | https://www.fishersci.com/shop/products/texwipe-cleantip-alpha-polyester-series-swabs-6/18383 |
Current Preamplifier | DL Instruments | 1211 | https://www.brltest.com/index.php?main_page=product_info&products_ id=1419 |
Custom PCB (w/ components) | n/a | n/a | see Supplemental files 4 and 5 |
DAQ Terminal Block | National Instruments | BNC-2120 | https://www.ni.com/en-in/support/model.bnc-2120.html |
DAQ to BNC-2110 cable | National Instruments | SHC68-68-EPM | https://www.ni.com/en-in/support/model.shc68-68-epm.html |
Data Acquisition Board (DAQ) | National Instruments | PCI-6251 | https://www.ni.com/docs/en-US/bundle/pci-6251-feature/page/overview.html |
Dessicator | Thermo Fisher Scientific | 5311-0250 | https://www.thermofisher.com/order/catalog/product/5311-0250 |
Female BNC To Banana Plug Adapter | Pomona Electronics | 72909 | https://www.digikey.com/en/products/detail/pomona-electronics/72909/1196318 |
Fetal Bovine Serum (FBS) | VWR | 89510-186 | https://us.vwr.com/store/product/18706419/avantor-seradigm-select-grade-usda-approved-origin-fetal-bovine-serum-fbs |
Glass Cutter | Chemglass | CG-1179-21 | https://chemglass.com/plate-glass-cutters-diamond-tips |
Gold Etchant TFA | Transene | NC0977944 | https://www.fishersci.com/shop/products/NC0977944/NC0977944 |
Hot Plate | Thermo Fisher Scientific | SP131825 | |
Isopropyl Alcohol | Spectrum Chemical | I1056-4LTPL | Purity (GC) ≥99.5% (https://www.spectrumchemical.com/isopropyl-alcohol-99-percent-fcc-i1056) |
Metal Hardware Enclosure | Hammond Manufacturing | EJ12126 | https://www.digikey.com/en/products/detail/hammond-manufacturing/EJ12126/2423415 |
Methanol | Sigma-Aldrich | 34860 | Purity (GC) ≥99.8% (https://www.sigmaaldrich.com/IN/en/substance/methanol320467561) |
MF-321 Developer | Kayaku Advanced Materials | n/a | https://kayakuam.com/products/mf-321/ |
MICROPOSIT S1813 Positive Photoresist | DuPont | n/a | https://kayakuam.com/products/microposit-s1800-g2-series-photoresists/ |
Phosphate Buffered Saline (PBS) | Thermo Fisher Scientific | 10010049 | https://www.thermofisher.com/order/catalog/product/10010049?SID=srch-hj-10010049 |
Photomask | Fineline Imaging | n/a | Photomask are custom ordered from our CAD designs (https://www.fineline-imaging.com/) |
Plain Glass Microscope Slide | Fisher Scientific | 12-553-5B | Material: Soda Lime, L75 x W50 mm, Thickness: 0.90–1.10 mm |
Plasma Cleaner | Harrick Plasma | PDC-001 | https://harrickplasma.com/plasma-cleaners/expanded-plasma-cleaner/ |
Plastic Petri Dish | Thermo Fisher Scientific | FB0875712 | 100 mm (https://www.fishersci.com/shop/products/fisherbrand-petri-dishes-clear-lid-raised-ridge-100-x-15mm/FB0875712) |
Pressure Controller | Fluigent | MFCS-EZ | https://www.fluigent.com/research/instruments/pressure-flow-controllers/mfcs-series/ |
Pressure Controller Software | Fluigent | MAESFLO | |
Programming & Computation Software | MATLAB | R2021b | for data acquisition and analysis (https://www.mathworks.com/products/matlab.html) |
PTFE Tubing | Cole Parmer | 06417-31 | 0.032" ID x 0.056" (https://www.coleparmer.com/i/masterflex-transfer-tubing-microbore-ptfe-0-032-id-x-0-056-od-100-ft-roll/0641731) |
Scepter 2.0 Handheld Automatic Cell Counter | Millapore Sigma | PHCC20060 | https://www.sigmaaldrich.com/IN/en/product/mm/phcc20060 |
Silicon Wafer | Wafer World | 2885 | 76.2 mm, Single Side Polished (https://www.waferworld.com/product/2885) |
Spin Coater | n/a | n/a | |
SU-8 3025 Negative Photoresist | Kayaku Advanced Materials | n/a | https://kayakuam.com/products/su-8-2000/ |
SU8 Developer | Kayaku Advanced Materials | n/a | https://kayakuam.com/products/su-8-developer/ |
Sygard 184 Polydimethlysiloxane | Dow Chemical | 4019862 | https://www.ellsworth.com/products/by-market/consumer-products/encapsulants/silicone/dow-sylgard-184-silicone-encapsulant-clear-0.5-kg-kit/ |
Tape | Scotch | 810-341296 | https://www.staples.com/Scotch-Magic-Tape-810-3-4-x-36-yds-1-Core/product_130567?cid=PS:GS:SBD:PLA:OS&gclid= Cj0KCQjwlK-WBhDjARIsAO 2sErRwzrrgjU0NjFkDkne1xm vT7ekS3tdzvAgiMDwPoxocgH VTQZi7vJgaAvQZEALw_wcB |
Titanium, Platinum, Gold | n/a | n/a | |
Triple Output Power Supply | Keysight | E36311A | https://www.newark.com/keysight-technologies/e36311a/dc-power-supply-3o-p-6v-5a-prog/dp/15AC9653 |
UV Mask Aligner | Karl Suss America | MJB3 Mask Aligner |