Microelectromechanical systems (MEMS) resonant sensors provide a high degree of accuracy for measuring the physical properties of chemical and biological samples. These sensors enable the investigation of cellular mass and growth, though previous sensor designs have been limited to the study of homogeneous cell populations. Population heterogeneity, as is generally encountered in primary cultures, reduces measurement yield and limits the efficacy of sensor mass measurements. This paper presents a MEMS resonant pedestal sensor array fabricated over through-wafer pores compatible with vertical flow fields to increase measurement versatility (e.g., fluidic manipulation and throughput) and allow for the measurement of heterogeneous cell populations. Overall, the improved sensor increases capture by 100% at a flow rate of 2 μL/min, as characterized through microbead experiments, while maintaining measurement accuracy. Cell mass measurements of primary mouse hippocampal neurons in vitro, in the range of 0.1-0.9 ng, demonstrate the ability to investigate neuronal mass and changes in mass over time. Using an independent measurement of cell volume, we find cell density to be approximately 1.15 g/mL.
Microelectromechanical systems (MEMS) resonant sensors provide a high degree of accuracy for measuring the physical properties of chemical and biological samples. These sensors enable the investigation of cellular mass and growth, though previous sensor designs have been limited to the study of homogeneous cell populations. Population heterogeneity, as is generally encountered in primary cultures, reduces measurement yield and limits the efficacy of sensor mass measurements. This paper presents a MEMS resonant pedestal sensor array fabricated over through-wafer pores compatible with vertical flow fields to increase measurement versatility (e.g., fluidic manipulation and throughput) and allow for the measurement of heterogeneous cell populations. Overall, the improved sensor increases capture by 100% at a flow rate of 2 μL/min, as characterized through microbead experiments, while maintaining measurement accuracy. Cell mass measurements of primary mouse hippocampal neurons in vitro, in the range of 0.1-0.9 ng, demonstrate the ability to investigate neuronal mass and changes in mass over time. Using an independent measurement of cell volume, we find cell density to be approximately 1.15 g/mL.
Microelectromechanical
systems
(MEMS) can accelerate biological and medical research by introducing
quantitative measurement devices capable of simultaneously handling,
manipulating, and characterizing individual cells.[1] The desire to study the growth of individual cells has
driven the development of cantilever,[2] suspended
microchannel,[3−5] and pedestal[6,7] resonant sensors, which
measure the mass of captured objects through the shift in device resonant
frequency. Studies of yeast,[4] human colon
cancer cells (HT29),[6] cervical cancer cells
(HeLa),[2] and bacterial cells[8] demonstrated that MEMS resonant mass sensors
are effective tools for measuring cellular growth rates. Recently,
we extended the use of MEMS resonant sensors for the characterization
of microscale hydrogel structures for tissue-engineering applications.[9,10]While cell lines are the population of choice for many cell
biology
studies, tissue-derived (primary source) cultures are a mainstay for
postmitotic cell populations. The process of generating primary, postmitotic
neurons in culture yields a highly mixed cellular population, which
presents additional challenges for single-cell studies. For over a
century, numerous culture devices and methods have provided ideal
microenvironments to glean insights into neuronal development.[11−13] MEMS sensor arrays[6,14,15] potentially provide a unique advantage for measuring the growth
of neurons, if neurons can be isolated from the heterogeneous population.
Neurochemical and cell signaling studies utilize neuronal growth measures
in vitro to measure the duration of the polarization process, axonal
elongation rates, and filopodial dynamics (space, time, and direction).[16−19] New techniques that allow for additional measures of neuronal growth
have the potential to aid in cell signaling studies and investigations
into the influence of neurotrophins, cytokines, and neurotoxins on
neuronal biomechanics (e.g., stiffness and biomass accumulation).To enable whole cell mass measurements of target cells within heterogeneous
populations for mass and growth analyses, further design requirements
and functionalities are required to increase sensor yield. The measurement
yield from MEMS resonant pedestal sensors are inherently limited by
the presence of objects on the sensor springs, which alters the effective
spring constant of the sensor and invalidates the measurement. The
stochastic process of random cell seeding in static fluid domains
provides a finite limit to the yield; we define sensor yield as the
number of functional sensors with appropriately captured objects that
provide accurate and reliable measurements. The measurement yield
is further challenged when studying heterogeneous populations as the
cells of interest (e.g., neurons) make up only a small fraction of
captured objects. To improve the efficiency of our MEMS mass sensor
array for heterogeneous populations, we redesigned the fabrication
process to incorporate vertical flow fields and on-chip microfluidic
channels that remove cells from the sensor springs to increase sensor
yield and enable high-throughput growth studies.This paper
reports the design, fabrication, and characterization
of a MEMS resonant mass sensor array, where each sensor is suspended
over a vertical microfluidic channel etched through the entire silicon
wafer. An additional PDMS-based microfluidic perfusion chamber and
a backside drainage chamber constitute an on-chip microfluidic system
and provide increased functionality. We demonstrate the feasibility
of improved capture efficiency through finite element flow simulations
and microbead capture experiments. We show that the vertical flow
pedestal sensors retain the native functionality of the original,
nonflow sensors and use them to measure the mass and growth of mouse
primary hippocampal neurons in vitro.
Experimental Section
Fabrication
of Vertical Flow MEMS Resonant Sensor Arrays
Figure 1 illustrates the key steps of the
fabrication process, which are outlined here. The starting material
was a silicon-on-insulator (SOI) wafer with a 2 μm thick silicon
device layer, a 0.6 μm buried oxide (BOX) layer, and a 500 μm
silicon handle layer, as depicted in Figure 1A. First, we grew a passivation layer of silicon dioxide (25 nm)
using thermal oxidation. After deposition of the passivation layer
(Figure 1B), a photolithography process patterned
the square pedestals and beam springs. Then, 10 nm of chromium and
50 nm of gold were deposited using thermal evaporation and patterned
with a liftoff process. Figure 1C shows the
device after the first liftoff process. Once the devices are defined,
a photoresist etch mask is patterned by photolithography along with
the first metal layer to create the sensor areas. An inductively coupled
plasma (ICP) etcher formed the springs and the platform using the
Bosch process, which etched the exposed silicon until it stops at
the BOX layer (Figure 1D). A second photolithography
patterned the electrodes for connecting the finished devices to printed
circuit boards. E-beam evaporation deposited another 100 nm of chromium
and 900 nm of gold, which were also patterned through liftoff. Figure 1E shows the resulting metallization of the electrodes,
which allows the bias current to flow through a single row of devices
at one time.
Figure 1
(A–G) Fabrication process for vertical flow MEMS
mass sensor
with backside pore. (H–J) Scanning electron microscope (SEM)
images of the resonant mass sensor array.
(A–G) Fabrication process for vertical flow MEMS
mass sensor
with backside pore. (H–J) Scanning electron microscope (SEM)
images of the resonant mass sensor array.Fabrication of the backside pore began after metallization.
Photolithographic
patterning of the wafer backside followed by an ICP etch, again using
the Bosch process, removed the 500 μm silicon handle layer from
beneath the platform sensor (Figure 1F). As
a result, microfluidic pores with smooth vertical sidewalls were formed
in the wafer beneath the sensor structure to permit fluid transport.
Next, a buffered oxide etch (BOE) removed the BOX layer, suspending
the devices over the backside pore (Figure 1, panels G–J). The final fabrication step deposited a 100
nm silicon dioxide layer for insulation, using a plasma-enhanced chemical
vapor deposition (PECVD) process. Prior to wire-bonding the resulting
chip to a printed circuit board, we selectively etched the PECVD oxide
on the bonding pads with BOE.
Perfusion Chamber Fabrication
and Assembly
Figure 2A depicts the
on-chip microfluidic system that includes
a microfluidic perfusion layer made of poly(dimethylsiloxane) (PDMS)
that receives flow from a syringe pump at a controlled rate during
cell capture. The perfusion layer divides the applied fluid through
bilaterally symmetric branching channels to the sensor array, and
Figure 2B presents the design of the microfluidic
channels.
Figure 2
Complete chip assembly with microfluidics and fluidic flow description.
(A) Schematic of assembled chip showing the PDMS-based microfluidic
perfusion layer on chip containing sensor arrays that allow vertical
flow through backside pore. (B) Schematic of channel architecture
for PDMS-based microfluidic perfusion layer (top-down view) designed
to distribute incoming fluid from a syringe pump across the sensor
array. (C) Scanning electron microscope (SEM) image of the microfluidic-tubing
interface and channel openings into the culture well for the section
of perfusion layer highlighted in the inset. (D) Magnified SEM image
of microfluidic channel openings for fluid infusion into the culture
well from the tubing and syringe pump. (E) Top and (F) side view images
of the fully assembled chip with the microfluidic layer, perfusion
tubing, and PDMS-based outlet drain beneath the PCB.
Complete chip assembly with microfluidics and fluidic flow description.
(A) Schematic of assembled chip showing the PDMS-based microfluidic
perfusion layer on chip containing sensor arrays that allow vertical
flow through backside pore. (B) Schematic of channel architecture
for PDMS-based microfluidic perfusion layer (top-down view) designed
to distribute incoming fluid from a syringe pump across the sensor
array. (C) Scanning electron microscope (SEM) image of the microfluidic-tubing
interface and channel openings into the culture well for the section
of perfusion layer highlighted in the inset. (D) Magnified SEM image
of microfluidic channel openings for fluid infusion into the culture
well from the tubing and syringe pump. (E) Top and (F) side view images
of the fully assembled chip with the microfluidic layer, perfusion
tubing, and PDMS-based outlet drain beneath the PCB.Fabrication of the microfluidic distributive channel
perfusion
layer started with creation of a negative mold of the desired channels
using SU-8 50 photoresist (Microchem; Newton, MA). SU-8 50 was spun
on a 4 in. silicon wafer to a height of 50 μm and was prebaked
in two steps: 10 min at 65 °C and then 30 min at 95 °C.
The wafer was exposed to a mask defining the fluidic channels, creating
the negative mold, followed by a two-step postexposure bake: 1 min
at 65 °C and then 10 min at 95 °C. The resulting mold is
developed in SU-8 developer for 2 min at room temperature, rinsed
with isopropyl alcohol, and hard-baked at 125 °C for 15 min.
PDMS, mixed at a ratio of 1:10 curing agent to prepolymer, was poured
over the negative mold, degassed, and allowed to cure between 2 and
16 h at 70 °C. Individual perfusion layers were cut from the
polymerized PDMS, and a “corner punch” technique created
all inlets and outlets.Figure 2C depicts
the corner punch used
to anchor the microfluidic tubing and supply fluid through the microchannels.
The microfluidic perfusion layer was first punctured from the patterned
side with a 1 mm dermal biopsy punch, creating a vertical channel
at the patterned inlet with a depth of half the PDMS thickness. The
second channel is created in a single, angle-changing motion that
starts from the side to meet the vertical channel using the same 1
mm biopsy punch while slightly deforming the PDMS to expel the material
punched from both channels. A PDMS thin film is covalently bonded
to the patterned piece, thus creating a sealed, embedded channel.
The PDMS layers are bonded through oxygen plasma activation in a barrel
etcher followed by a 70 °C bake for 15 min. A 6 mm dermal biopsy
punch is then pressed through the 4 mm thick PDMS microfluidic system
to define the culture chamber and open the microfluidics into the
culture well (Figure 2D). Finally, the PDMS-based
well, with embedded microfluidics, is sealed to the MEMS sensor array,
following oxygen plasma activation, alignment, and heating (Figure 2, panels E–F). PTFE ultramicrobore
tubing (Cole-Parmer; Vernon Hills, IL) makes fluidic connections between
the chip and syringe pumps; the curvature of the corner punch assists
in retaining the tubing in place while providing a good seal (Figure 2, panels E–F). A Harvard Apparatus PicoPlus
syringe pump (Holliston, MA) delivers constant stream of fluid into
the system through a T-connector to split the flow for equal distribution
to both fluidic inlets.
Fluid Flow Modeling
We modeled the
velocity characteristics
of the flow around the sensor and through the backside pore using
the finite element method (FEM). Simulation of steady-state incompressible
flow in the system used the Navier–Stokes equations and the
geometry of a single sensor and channel in COMSOL Multiphysics 3.5a
(COMSOL; Burlington, MA). Boundary conditions for the incompressible
Navier–Stokes equations included: no slip at the interface
with pore walls and the sensor, a set velocity uniform across the
inlet, and a zero-pressure condition at the outlet with no viscous
stress. We computed velocity fields for three flow rates: 2, 4, and
8 μL/min. This rate of total flow delivered to all sensors was
converted to velocity at the inlet of each individual channel, assuming
even distribution between sensors and a uniform velocity at the inlet.
Capture Efficiency Characterization
We characterized
the capture efficiency of the vertical flow sensor and compared with
the previous generation of sensor design, which included platforms
suspended over shallow pits without microfluidics for cell measurements
in a ∼100 μL static well. We will refer to these older
sensors as “pit” sensors throughout the rest of the
text. Aqueous solutions of 15 μm polystyrene beads in phosphate
buffered saline (PBS) with bead densities of 9000 and 18000 beads
per 28 mm2 area were evenly mixed and dispersed onto the
sensor array. Beads in solution settled for 10 min prior to sealing
the chamber with a glass coverslip. For each bead density, the syringe
pump forced PBS through the PDMS microfluidic channel system for 30
min at the specified rate (2, 4, and 8 μL/min). For pit sensors,
which receive no flow, beads settled in a static bath for 30 min.
We monitored bead capture through images acquired with a Spot flex
monochrome camera (Diagnostic Instruments; Sterling Heights, MI) attached
to an Olympus BX51 upright fluorescent microscope (Olympus America
Inc.; Center Valley, PA) at an acquisition rate of one image per minute
for a 30 min capture period. We repeated the experiments three times
for each flow rate and bead density, for a total of 24 experiments.
Cell Culture and Mass Measurements
Following previously
established protocols,[20] we isolated cells
from the enzymatically digested hippocampus of EGFP-actin mice (C57BL/6-Tg(CAG-EGFP)1Osb/J),
Jackson Laboratories; Bar Harbor, ME). Mice were used in accordance
with protocols established by the University of Illinois Institutional
Animal Care and Use Committee and in accordance with all state and
federal regulations. Cells were maintained in supplemented Hibernate-A
or Neurobasal-A (Invitrogen; Carlsbad, CA) supplemented with 0.5 mM l-glutamine, Gem21 NeuroPlex (Gemini Bio-Products; West Sacramento,
CA), 100 units/mL penicillin and 0.1 mg/mL streptomycin under standard
culture conditions at 37 °C during growth measurements. Neurobasal
and Hibernate are defined media formulations optimized for enriching
neuronal growth at low densities and selecting against most mitotic
cells.[21,22]Mass measurement of cells relies on
estimating the resonant frequency shift between empty and loaded sensors,
a process that is well-characterized,[6,7] and will only
be briefly described here (Figure 3). Resonant
frequency is determined through electromagnetic actuation of the sensors
and concurrent velocity measurements with a laser Doppler vibrometer
(LDV) system housed on a Zeiss Axiotech Vario upright microscope (Carl
Zeiss AG; Jena, Germany). Prior to seeding the sensors with cells,
two measurements are made on the empty sensors. First, the resonant
frequency of each sensor in air is measured to determine the effective
spring constant for each device, assuming negligible damping. Second,
the resonant frequency of the sensors in cell culture media is measured
to account for the change in resonant frequency from damping and hydrodynamic
loading.[23] Finally, dissociated cells are
seeded and captured onto the sensor array and the resulting resonant
frequencies of the sensors loaded with cells are measured. Measuring
these frequencies allows for the extraction of the adhered mass of
the cells on the platform from the final measured resonant frequency
of the loaded sensors.
Figure 3
Operation and characterization of vertical flow resonant
sensor
array. (A) Overview of the mass measurement setup. (B) Distribution
of the sensor spring constant, a sensor array fabricated with vertical
flow channels; distribution of the sensor resonant frequency while
submerged in fluid and subject to different applied flow rates; and
the variation in sensor resonant frequency with flow applied.
Operation and characterization of vertical flow resonant
sensor
array. (A) Overview of the mass measurement setup. (B) Distribution
of the sensor spring constant, a sensor array fabricated with vertical
flow channels; distribution of the sensor resonant frequency while
submerged in fluid and subject to different applied flow rates; and
the variation in sensor resonant frequency with flow applied.
Volume Measurements and
Immunocytochemistry
Following
mass measurement, cells were fixed (4% paraformaldehyde in PBS, 30
min) for volume estimation with confocal microscopy and cellular identification
through immunocytochemistry. The volume of measured cells was obtained
using a Zeiss LSM 700 laser scanning confocal microscope with an argon
laser (488 nm) and a Plan-Apochromat 20×/0.8
objective (Carl Zeiss Microscopy GmbH; Jena, Germany). Confocal image
stacks (Z-stacks) with a 0.63 × 0.63 × 0.65 μm3 voxel size were acquired for cell volume calculations using
Amira 5.4.1 (Visualization Sciences Group; Mérignac, France).Cellular identities of neurons were achieved using immunocytochemistry.
Cells on sensors were permeabilized with 0.25% Triton-X 100 in PBS
for at least 5 min. Samples were blocked from nonspecific antibody
binding with 5% bovine serum albumin in PBS, followed by rabbit polyclonal
primary antibody incubation for the neuronal marker microtubule associated
protein-2 (MAP2) (1 h, room temperature). Secondary antibody incubation
(goat-anti rabbit, Alexa 568) was performed (1 h, room temperature)
prior to imaging with the Spot Flex camera on upright microscope.
Results and Discussion
We fabricated MEMS resonant pedestal
mass sensors with vertical
flow microfluidic pores etched through the wafer. This sensor array
enables fluid-exchange during mass measurements without interruption,
thereby enabling a greater variety of studies than previously available.[6,9,10] This new on-chip microfluidic
system allows for delivery of cells, culture media, and chemical agents
to the cells on sensors, while providing a method for removing unwanted
cells from the sensor springs to improve sensor measurement yield.
Figure 1 shows scanning electron microscope
(SEM) images of the fabricated sensor with a backside pore designed
to accommodate constant fluid flow. Figure 1H shows a single sensor, which consists of a pedestal suspended by
four beam springs over the backside pore. Etching completely through
the wafer backside produces the pores (Figure 1I) that constitute an on-chip microfluidic system when combined with
a horizontal PDMS-based, embedded microfluidic perfusion layer (Figure 2). Each chip contains 81 sensors (arranged in a
9 × 9 array) for high measurement throughput (Figure 1J).Cell mass measurements require operation
of the sensors in the
first resonance mode to ensure uniform mass sensitivity where the
average platform vibrates vertically at 167 ± 10 kHz in-air and
68 ± 5 kHz in-liquid, owing to an average spring constant of
18 ± 2 N/m (Figure 3). These values compare
well with those of the previous generation “pit” sensor,
which exhibited 152 ± 7 kHz and 63 ± 3 kHz in-air and in-liquid
resonant frequencies, respectively. The difference in resonant frequency
between the vertical flow field sensors introduced here and the pit
sensors is due to the variation in wafer and sensor layer thicknesses
during manufacturing and microfabrication.[24]It is critical that the incorporation of flow-exchange capabilities
(sensor structure and flow fields) do not affect the sensor measurements.
Mass is estimated through measurement of the device resonant frequency
using an LDV system (Figure 3A). Figure 3B shows the range in spring constants of all mass
sensors on a typical chip and their resonant frequencies at different
flow rates. Essentially, there is less than 1% deviation in the resonant
frequency with applied flow rates of 2 and 4 μL/min compared
with no flow resonant frequency. In comparison to our previous sensor
arrays (frequency drift = 100–200 Hz/day),[6] Figure 3B shows that resonant frequency
drift of the new sensor is similar (slope of drift is 80 Hz over 24
h) in the presence of media flow at both 2 and 4 μL/min. Much
higher flow rates may be desirable for certain applications, though
it is likely that the increased hydrodynamic loading at higher flow
rate will alter the resonant frequency and quality factor,[23] which should be considered during experimental
design.We performed simulations using finite element analysis
to calculate
and visualize the flow velocity profiles through the channel (Figure 4A). The fluid velocity around the sensor pedestal
and springs is of particular interest since it will govern the cell
capture characteristics. Modeling data shows a low velocity field
above the pedestal sensor, which is also the cell attachment area,
and this low velocity field appears to remain unaffected by media
flow. In contrast, high velocity fields exist around the springs to
deter cell attachment.
Figure 4
Fluidic modeling and experimental sensor capture efficiency.
(A)
Simulation of the microfluidic vertical flow field comprising the
fluid-filled space and the MEMS sensor platform or spring (top of
image). (B) Experimental capture efficiency comparison of the no flow
pit sensor with the vertical flow sensor, which shows approximately
100% increase in capture efficiency; inset shows an example of beads
captured on platforms and springs. (C) Simulation results of the capture
region area/volume for the different applied flow rates from 2 to
8 μL/min on the springs (left) and the sensor (right).
Fluidic modeling and experimental sensor capture efficiency.
(A)
Simulation of the microfluidic vertical flow field comprising the
fluid-filled space and the MEMS sensor platform or spring (top of
image). (B) Experimental capture efficiency comparison of the no flow
pit sensor with the vertical flow sensor, which shows approximately
100% increase in capture efficiency; inset shows an example of beads
captured on platforms and springs. (C) Simulation results of the capture
region area/volume for the different applied flow rates from 2 to
8 μL/min on the springs (left) and the sensor (right).To demonstrate improvements in
capture efficiency over the previous
sensor technology, we seeded beads on both new vertical flow sensors
and on the pit sensor array (an array without flow capabilities).
Figure 4B shows the capture efficiency of the
beads on the pit sensor versus the flow sensor with various flow rates,
along with an example image of a captured bead. Two-way ANOVA tested
the dependence of capture efficiency on both seed density and flow
rate. We found capture efficiency exhibits a statistical dependence
on flow rate (p = 0.016), with a maximum occurring
at 2 μL/min. There is also dependence on seed density (p = 0.001), with maximum capture efficiency at 18000 beads
per 28 mm2 diameter.Figure 4C shows “capture regions”
around the springs and sensor defined by thresholding the simulated
velocity field at 5 μm/s. These regions help explain why the
apparent maximum efficiency appears to occur at 2 μL/min and
not at higher flow rates. The capture zone around the spring for the
2 μL/min suggests that an object larger than approximately 5
μm will be affected and prevented from settling on the springs.
While the capture zone shrinks with higher flow rate, this should
have no additional effect on the beads used in this experiment, which
are 15 μm in diameter. However, the increased flow rate also
reduces the capture region of the sensors. This will have the effect
of dragging beads off the sensors; especially those not captured near
the center.In practice, the number of useable sensors with
viable neurons
is further reduced from the stochastic capture rate due to the presence
of other cells in the sample. While the hippocampus is a structurally
defined region of brain tissue that is easy to excise and dissociate
for developmental studies of neuronal cells in vitro,[25] the process of cellular extraction yields a mixed population
of neurons, glial cells, microglia, and endothelial cells.[22] Defined media formulations have been produced
to sustain neurons in culture, while selecting against non-neuronal
and mitotic cell types.[22] In addition to
a media-dependent population selection, neurons in culture develop
at different rates dependent upon the stage of in situ cell development
at the time of neuronal isolation.[26] Therefore,
different maturation rates, or durations of “time-to-polarization,”
will also be observed.To better understand the probability
of capturing neurons, particularly
differentiating neurons and their non-neuronal cellular counterparts,
we performed morphometric and immunocytochemical analyses of age-matched
cultures to characterize the cellular populations present in our experiments
(Figure 5). From phase contrast imaging of
the cell population in culture, we predict that living adherent cells
(spherical or ramified) will be approximately 40% of all cells captured
on the sensors in our studies, while approximately 20% of the living
cells (8% of total cells) will have ramified processes reminiscent
of neuronal growth after 15 h in culture (Figure 5A). Further, immunocytochemical staining of the culture population
revealed that neurons account for 60% of the living cells in culture,
while the remaining 40% are non-neuronal cells, such as glia (Figure 5B).[27] Figure 5C presents the predicted efficiency of capturing
living brain cells on the platform sensors, derived by multiplying
the results from the observations in culture with the capture efficiency
from the microbead experiments. The capture efficiency of neurons
on pit sensors is estimated at 4.5%, while flow sensors should exhibit
8.4% capture efficiency. With dependence on the cell type of interest,
experimental parameters will need to be optimized for capturing adherent
cells, bearing in mind that flow velocity regions scale appropriately.
The resulting optimized capture efficiency will also depend on factors
such as cell geometry, buoyancy, adhesion, and viscoelasticity that
can influence retention on the sensor area. For example, cell adhesiveness,
a topic of intense research for decades,[28−30] is cell- and
substrate-dependent;[31] therefore, cell
capture dynamics will vary, depending on the population under investigation.
Figure 5
Cell capture
analysis for heterogeneous populations of primary
EGFP-transgenic mouse brain cells on MEMS resonant mass sensor arrays.
(A) Phase contrast microscopy and (B) fluorescence microscopy images
of 15-hour, age-matched cultures of EGFP-actin transgenic mouse primary
hippocampal neurons in flasks and on silicon chips for population
analysis. (C) With the use of the primary neuron population characteristics
with expected capture efficiency from microbead experiments, we predict
the capture efficiency of neurons to be 4.5% for the pit sensor and
8.4% for the flow sensor.
Cell capture
analysis for heterogeneous populations of primary
EGFP-transgenicmouse brain cells on MEMS resonant mass sensor arrays.
(A) Phase contrast microscopy and (B) fluorescence microscopy images
of 15-hour, age-matched cultures of EGFP-actin transgenicmouse primary
hippocampal neurons in flasks and on silicon chips for population
analysis. (C) With the use of the primary neuron population characteristics
with expected capture efficiency from microbead experiments, we predict
the capture efficiency of neurons to be 4.5% for the pit sensor and
8.4% for the flow sensor.Dissociated cells from the mouse hippocampus were seeded
on sensor
arrays for mass measurements at a single time point. Cells were then
fixed for immunolabeling and confocal microscopy to estimate cell
volume. Figure 6A plots the cell mass and volume
of each cell measured and marked by the identified cell type, where
the measured mass ranges from 0.1 to 0.9 ng. The slope of the linear
fit indicates the density of the cells to be approximately 1.15 ±
0.04 g/mL, which is similar to reported ranges for nonadherent murine
lymphocytes and human erythrocytes.[5] The
Pearson linear correlation test of mass and volume returned a R2 of 0.97 and p-value <
0.001, indicating that the strong correlation between the two measures
is statistically significant.
Figure 6
(A) Mass of cells estimated with resonant sensors
shows a strong
linear relationship with estimates of cell volume obtained through
confocal microscopy. Red ▲ mark neuronal cells and blue ●
mark non-neuronal cells, while an unidentifiable cell is indicated
by □. (B) The apparent mass of brain cells after fixation is
1.05 times greater than before fixation. (C) Schematic of dynamic
model demonstrating the two-degree-of-freedom (2DOF) mass-spring-damper
system. (D) An overview three-dimensional plot showing how stiffness
and viscosity affect the result of the 2DOF system. (E) Cross-section
of a neuron, indicating the height and width values, which was reconstructed
in Amira for 3D data visualization and analysis. (F) Shows the apparent
mass from the sensor to the actual mass ratio and how that ratio is
directly affected by the shape of the cell.
(A) Mass of cells estimated with resonant sensors
shows a strong
linear relationship with estimates of cell volume obtained through
confocal microscopy. Red ▲ mark neuronal cells and blue ●
mark non-neuronal cells, while an unidentifiable cell is indicated
by □. (B) The apparent mass of brain cells after fixation is
1.05 times greater than before fixation. (C) Schematic of dynamic
model demonstrating the two-degree-of-freedom (2DOF) mass-spring-damper
system. (D) An overview three-dimensional plot showing how stiffness
and viscosity affect the result of the 2DOF system. (E) Cross-section
of a neuron, indicating the height and width values, which was reconstructed
in Amira for 3D data visualization and analysis. (F) Shows the apparent
mass from the sensor to the actual mass ratio and how that ratio is
directly affected by the shape of the cell.We also repeated each mass measurement after fixation, and
Figure 6B shows the comparison of measured
mass before and
after fixation. This type of fixation measurement was previously used
to demonstrate how the apparent measured mass is a function of the
viscoelasticity of the measured cell,[6] with
the measured mass of soft materials deviating from the actual mass.
This apparent mass difference is explained by a two-degree-of-freedom
(2DOF) dynamic system modeling the cell mass oscillating out-of-phase
with the platform sensor (Figure 6C).[10] This oscillation causes an additional resonant
frequency shift, and Figure 6D depicts how
this changes the apparent mass based on material properties. Since
fixation causes a significant stiffening of tissue,[32,33] it is expected that the apparent mass measured before and after
fixation should not be the same. However, from the slope of the fit
line in Figure 6B, we observed an apparent
mass ratio of approximately 1.05, which is lower than described in
our previous study on a different cell type (humancancer cell line).[6] While the previously reported neuron stiffness
of 1 kPa[34] could produce a more significant
deviation, it should be noted that the effective stiffness and damping
ratio in Figure 6 (panels C and D) depend not
only on the material properties but also on the shape of the object.
Treating the cell as a cylinder and using the elastic modulus of 1
kPa and assuming a viscosity of 1 mPa s, we explored how the low profile
of the brain cells (Figure 6E) greatly affects
the apparent mass ratio estimated from the fixation measurement (Figure 6F). As a cell gets shorter and wider, the apparent
mass ratio approaches unity. The adherent cells investigated had a
very high radius to height ratio (>5), and thus, the apparent mass
exhibited only a small deviation from actual mass.The prototypical
neuronal marker MAP2 identifies somatodendritic
structures of neurons. MAP2 immunolabeling is used to characterize
hippocampal neurons of EGFP transgenic mice, following mass measurement.
Immature neurons differentiate by extending primary neurites, which
then differentiate into an axon (longest process) and dendrites. While
conventional culture protocols implement cell adhesion molecules for
neuronal attachment, neurons show prototypical growth, differentiation,
and adhesion on native silicon oxide surfaces. Figure 7 (panels A–I) demonstrates that all stages of neuronal
development are present on our chips and sensors. Even disconnected
dendrites are observable in culture adhering to the sensor surface.
Figure 7
Mass and
growth of neurons and glial cells measured by MEMS resonant
mass sensors. The heterogeneous population of seeded cells leads to
the capture of neuronal clusters or individual neurons captured on
sensors (A–B) or even subcellular fragments, such as dendrites
identified by size and high MAP2 expression (C–D). The captured
neurons vary in development and differentiation states ranging from
undifferentiated to polarized morphologies (E–H) and are easily
distinguished from (I) suspected glial cells. (J) Pictographic summary
characteristic of early neuronal growth and differentiation, redrawn,
and modeled after previous descriptions.[35] (K) Mass of four individual cells measured with vertical flow resonant
sensors. Putative neuronal growth (solid lines) and cell selection
and death (dashed lines) are observable from measured growth profiles.
Mass and
growth of neurons and glial cells measured by MEMS resonant
mass sensors. The heterogeneous population of seeded cells leads to
the capture of neuronal clusters or individual neurons captured on
sensors (A–B) or even subcellular fragments, such as dendrites
identified by size and high MAP2 expression (C–D). The captured
neurons vary in development and differentiation states ranging from
undifferentiated to polarized morphologies (E–H) and are easily
distinguished from (I) suspected glial cells. (J) Pictographic summary
characteristic of early neuronal growth and differentiation, redrawn,
and modeled after previous descriptions.[35] (K) Mass of four individual cells measured with vertical flow resonant
sensors. Putative neuronal growth (solid lines) and cell selection
and death (dashed lines) are observable from measured growth profiles.Finally, to demonstrate the functionality
of this MEMS mass sensor
array for investigating neuronal cell growth, we performed preliminary
growth measurements of primary, dissociated postnatal mouse cells
from the hippocampus of the EGFP-expressing transgenicmouse. Methods
for culturing primary neurons in defined media render nearly pure
neuronal populations at about 4 days in culture. Figure 7J shows a schematic representation of early prototypical neuronal
growth and differentiation in vitro. Figure 7K shows growth profiles for 4 cells captured on the vertical flow
MEMS sensor array. After initial seeding, non-neuronal mitotic cells,
which are abundant in primary cultures, begin to die off, while neurons
grow and differentiate. Two of the cells exhibit growth, while the
remaining two cells show an abrupt mass decrease without recovery
as early as 7 h in vitro, and may mark the death of non-neuronal cells.
Previous studies using these sensors show that the apparent mass increase
observed by the pedestal sensor represents true cell growth.[6]Mass growth profiles of primary neurons
in culture have not been
previously explored; our preliminary data shows an increase in mass
growth followed by a plateau, which could be reminiscent of the internal
commitment to axonal specification of neurons. The establishment of
neuronal polarity (i.e., the extension and differentiation of neurites
into axons and dendrites) is very well-defined.[35] Dissociated neurons in vitro begin to send out immature
neurites (typically 3–5 neurites), which remain approximately
equal in length until one of the processes becomes comitted to form
an axon. After axonal specification, the axon exhibits robust growth
to become the longest process, while the remaining processes commit
to a dendritic fate and exhibit a slower growth rate. Our mass sensor
provides an aggregate measurement of neuronal growth, or non-neuronal
death, and is not capable of measuring the growth of each process.
The advancements and improvements made to the sensor and demonstrated
within this body of work allow for the measurement of the physical
properties of individual neurons and can enable investigations of
neuronal growth and differentiation. Future studies can couple mass
measurements with other methods for capturing additional information
on cellular morphology to better resolve growth with respect to specific
cell state and axonal process development.
Conclusion
To
overcome the challenges associated with investigating heterogeneous
cell populations in primary culture with MEMS mass sensors, we designed
and fabricated a platform resonant sensor array with backside pore
and integrated microfluidics. The on-chip microfluidic system allows
for the constant supply of cellular growth media and also provides
the means to increase removal of objects captured on sensor springs
to improve capture efficiency and measurement yield. Characterization
of device capture efficiency demonstrated a 2-fold increase over the
previous generation sensor that did not allow for fluid flow. We used
the resonant sensor with vertical flow fields to measure the mass
of heterogeneous cells harvested and dissociated from the mouse hippocampus.
The measured mass, ranging from 0.1 to 0.9 ng, shows strong agreement
with independent measurements of cell volume from confocal microscopy
and reveals the cell density to be approximately 1.15 g/mL. Growth
profiles of immature neurons correspond with the characteristic developmental
process of neuronal development, while growth profiles of non-neuronal
cells reveal death in defined media as early as 7 h in vitro. Further
studies of neuronal growth dynamics with this MEMS resonant sensor
array may allow for the study of neuronal differentiation and selection
with high measurement yield.
Authors: William H Grover; Andrea K Bryan; Monica Diez-Silva; Subra Suresh; John M Higgins; Scott R Manalis Journal: Proc Natl Acad Sci U S A Date: 2011-06-20 Impact factor: 11.205
Authors: Michel Godin; Francisco Feijó Delgado; Sungmin Son; William H Grover; Andrea K Bryan; Amit Tzur; Paul Jorgensen; Kris Payer; Alan D Grossman; Marc W Kirschner; Scott R Manalis Journal: Nat Methods Date: 2010-04-11 Impact factor: 28.547
Authors: Damith E W Patabadige; Larry J Millet; Jayde A Aufrecht; Peter G Shankles; Robert F Standaert; Scott T Retterer; Mitchel J Doktycz Journal: Sci Rep Date: 2019-07-16 Impact factor: 4.379