2.1.

Microwell Design

Our approach is based on enclosing single cells in hermetically sealed fused silica (glass) microwells of about 140 pL volume containing extracellular optical sensor material embedded in a polymer matrix. Ratiometric measurements of sensor emission intensity changes in response to oxygen concentration alterations were conducted using this sealed microwell and optical sensing phenotype characterization apparatus. In the current implementation $3\times 3$ arrays of symmetrically arranged microwells (50 μm ID and 20 μm deep) and matching lids [80 µm ID and 20 µm deep; Fig. 1(a), 1(b)] were used. Lids and microwells were fabricated in fused silica substrates using wet-etch lithography.²⁹ The hermetic seal was produced by placing lipped lids containing the extracellular oxygen sensor on top of the microwells and applying pressure to the lid array [Fig. 1(c), 1(d)]. A layer of compliant material was placed between the piston and the lid substrate to ensure equal force distribution across the lids.

Fig. 1

Microwell array design and characterization. (a) Design of the upper (lid) and lower (well) parts of the microwells used in this study. Different designs were tested to optimize seal integrity for high oxygen sensitivity. Dimensions were chosen to provide uncomplicated cell loading into wells. (b) Topographic images of wells and lids obtained utilizing noncontact optical profilometry. $3\times 3$ arrays of wells and lids with 300-μm center-to-center spacing were fabricated on fused silica wafers using hydrofluoric acid (HF) deep wet etch lithography. Well and lid sidewalls show isotropic etch profile (insets). Average surface roughness was $\sim 16\mathrm{nm}$ for 20-μm etch depth. Overall process yield and repeatability was $>90\%$.²⁹ (c) $3\times 3$ array of microwells and lids microfabricated in fused silica substrate, and (d) hermetically sealed microwells with a total volume of 140 pL.

2.2.

Cell Culture

For this study, we used two immortalized human esophageal epithelial cell lines, CP-A and CP-C, derived from patients with Barrett’s esophagus without dysplasia and with dysplasia, respectively.³⁰ Cells were cultured in T75 tissue-culture flasks (Corning, Corning, NY) to approximately 80% confluence, at which time they were trypsinized, centrifuged at 900 rpm for 3 min, and resuspended in 2 mL of cell growth medium.

The CP-A and CP-C cells were grown at 37°C, under 5% ${\mathrm{CO}}_2$ atmosphere in cell culture flasks using GIBCO® Keratinocyte SFM cell growth medium (Invitrogen, Carlsbad, CA), supplemented with hEGF (Peprotech, Rocky Hill, NJ) at $2.5\mathrm{\mu g}/500\mathrm{mL}$, bovine pituitary extract (BPE) at $25\mathrm{mg}/500\mathrm{mL}$ and penicillin/streptomycin solution (Invitrogen) at $100\mathrm{units}/100\mathrm{\mu g}/\mathrm{mL}$. Prior to loading, cells were detached from the flask bottom using 0.05% trypsin-ethylenediaminetetraacetic acid (EDTA) solution and transferred to a Petri dish.

2.3.

Cell Loading into Microwells

Individual cells were loaded using a custom microfluidic platform³¹ optimized to enable aspirating and dispensing one or multiple cells into each microwell. The cell loader is built around a custom designed high-precision diaphragm micropump that allows for subnanoliter volumes to be aspirated and dispensed in a highly controlled manner.³² Single cells were aspirated and dispensed using a glass micropipette with a tip of 40 µm inner diameter attached to the pump. To accomplish the loading of a single cell, the cell of interest was brought into the center of the field of view of the microscope objective. Then, the tip of the micropipette that was attached to the micropump was lowered and positioned within 40 to 70 μm from the cell, and then the cell was aspirated into the micropipette by the micropump. Once the aspiration step was complete, the micropipette tip was raised and aligned with a second Petri dish containing the microwell substrate using a motorized microscope stage.

After trypsinization, the cells were loaded into microwells immediately to prevent cells from adhering to the bottom of the Petri dish. Glass substrates containing $3\times 3$ arrays of microwells were glued to the bottom of a Petri dish with a precut hole of a slightly smaller diameter than the substrate using medical-grade epoxy glue (K45-S-14ML, Chemical Concepts, Huntingdon Valley, PA). The Petri dish containing the microwell array was placed on the cell loader platform adjacent to the Petri dish containing suspended cells. For single-cell experiments, loading was performed by aspirating one or several cells into the micropipette at a time. For the experiments with 2 or 3 cells per well, individual cells were aspirated and loaded in the microwells one at a time. This was necessitated by the need for higher precision in the fluid flow control required to prevent an already loaded cell from being ejected from a well while loading the next one. The dispensing step was conducted using a computer-controlled vision-feedback algorithm that facilitated the release of one cell at a time from the micropipette tip; this required 5 to 8 min to load a $3\times 3$ array of microwells with 1 cell per well. Loading 2 or 3 cells per well in a $3\times 3$ array required 15 to 25 min.

After loading cells into the microwells, the arrays were incubated for 16 to 24 h under normal physiologic conditions to allow for cell adhesion and recovery from potential stress caused by manipulation [Fig. 2(a)]. No lid was placed on top of the wells during incubation to ensure cell access to nutrients and oxygen. We assessed cell viability after cell loading and incubation using the CalceinAM/Sytox Orange live/dead assay (Invitrogen, Carlsbad, CA). We found $\mathrm{that}>99\%$ of cells loaded in microwells showed enzymatic activity. Five to 10% of loaded cells divided in the microwells during the incubation time, which suggests excellent cell health and near-normal function after confinement in microwells. We account for these microwells by using transmission bright-field imaging to determine the number of cells in each microwell prior to experiments. Wells containing cells that underwent division during the incubation time are not included in the data analysis.

Fig. 2

Experimental single-cell respirometry platform. (a) Micrographs of microwells with 50-µm ID containing one (top), two (middle), and three (bottom) CP-A cells incubated 22 h after cell loading; and (b) single-cell OCR experimental setup. Prior to producing gas impermeable seal, lid and microwell were aligned using motorized rotation stage and manual XYZ stage mounted on the microscope XY stage. Inset: close-up view of microwell produced by placing array of lids over microwells containing single cells.

2.3.1.

Cell loading software overview and description

The cell loading software was implemented as a LabVIEW (National Instruments, Austin, TX) program used to automate the cell loading process, i.e., the process of selecting cells from a source Petri dish and transferring them to a selected destination microwell array for use in OCR experiments. Although the main LabVIEW program provides automation for cell loading-specific tasks, the system software uses generic laboratory automation code libraries to provide access to and automation of the system components.

The user interface is a LabVIEW program that allows the user to select from a variety of automation tasks required to accomplish the cell loading process. These tasks include: automated positioning of the Petri dish over the objective lens and under the transfer pipette, automated positioning of the selected microwell array over the objective and under the transfer pipette, raising and lowering of the pipette, and control of the picopump (Fig. 3).

Fig. 3

Cell loading software architecture overview.

2.4.

Sensor Deposition and Characterization

To quantify oxygen concentration in the microvolume surrounding a cell we used a self-referencing, ratiometric measurement of sensor emission intensity. The main advantage of this approach is that the signal is independent of deposited sensor volume. We used platinum porphyrin derivative, Pt(II) Octaethylporphine (PtOEP, Frontier Scientific, Logan, UT) as the oxygen sensor and silicon octaethylporphine (SiOEP, Frontier Scientific) as the reference luminophore. For deposition in the microwells we used 1 mg PtOEP (${\mathrm{O}}_2$ sensor) and 1 mg SiOEP dissolved in 1 g of monomer ethyoxylated-(3)-trimethylolpropane triacrylate (SR454, Sartomer, Exton, PA) solution containing 10 mg azobisisobutyronitrile (AIBN, Sigma-Aldrich, St. Louis, MO). AIBN was used as a thermal initiator of free radical polymerization of SR454. The mixture was sonicated until a homogenous solution was obtained and then stored at 4 °C until used.

The sensor was deposited in lids using a noncontact piezoelectric liquid dispensing robot (Rainmaker au301, Aurigin Technology Inc., Phoenix, AZ). We deposited 100 to 200 pL of sensor material in each microwell lid. The deposition step took approximately 200 μs for each lid (Fig. 4). The fused silica (interior lid) surface receiving the sensor material droplet was functionalized with a companion moiety designed to covalently bind to the SR454 polymer matrix. Prior to sensor deposition the lid surface was cleaned, plasma-treated, and functionalized with trimethylsilylpropyl acrylate (TMSPA) by vapor deposition. TMSPA binds to the hydroxyl radicals on the fused silica surface generated by the plasma treatment. SR454 monomer containing the sensor binds covalently to the acrylate moiety of the surface-bound TMSPA during thermal polymerization. After deposition the substrates were placed into a vacuum drying oven (DX400, Yamato, Santa Clara, CA) and thermally cured for 3 h at 80°C under nitrogen atmosphere ($70\mathrm{mm}\mathrm{Hg}/0.01\mathrm{MPa}$).

Fig. 4

Sensor deposition. (a) Schematic representation of piezoelectric sensor dispenser head (left); micrograph of glass capillary with nozzle (middle); micrograph of 40-μm nozzle orifice (right). (b) Stroboscopic time series of sensor droplet deposition. Sensor material is dissolved in SR454 monomer. Time series shows formation of $\sim 100\mathrm{pL}$ ($D\sim 57\mathrm{\mu m}$) droplet of SR454 by focusing acoustic wave produced by piezoelectric element at convergent orifice of capillary. Dispensing of droplet takes 200 μs.

Although the PtOEP phosphorescence signal depends strongly on oxygen concentration (${\mathrm{O}}_2$) in the surrounding medium, the SiOEP fluorescence intensity remains constant over a wide range of ${\mathrm{O}}_2$ [Fig. 5(a)]. The sensor response to changes in ${\mathrm{O}}_2$ was calibrated using reference solutions containing known concentrations of dissolved oxygen. The reference aqueous solutions were prepared by purging water or cell culture media with ${\mathrm{N}}_2+{\mathrm{O}}_2$ gas mixtures of the desired oxygen concentrations obtained using a computer controlled gas manifold (Alicat Scientific, Tucson, AZ). The calibration data were used for oxygen concentration determination in the OCR measurements. Fig. 5(b) shows calibration of the sensor in lids submersed in cell media. The measured data points were fit with the Stern-Volmer equation to create a continuous calibration curve. The goodness of fit supports the assertion that sensor emission intensity is governed by diffusion-limited dynamic quenching by oxygen.

Fig. 5

Ratiometric oxygen sensor calibration. (a) Emission spectra and intensity response of combined SiOEP and PtOEP sensor in SR454 thin-film matrix at various oxygen concentrations. Reference dye SiOEP has emission maximum at 575 nm. Emission intensity of SiOEP does not change with ${\mathrm{O}}_2$. PtOEP emission intensity, with maximum at 647 nm, exhibits strong inverse dependence on ${\mathrm{O}}_2$. (b) Calibration of PtOEP/SiOEP sensor after deposition in lids. Different ${\mathrm{O}}_2$ concentrations were obtained utilizing a high-precision gas mixing manifold. Solid line represents a Stern-Volmer fit to the calibration data.

2.5.

Experimental Setup

The OCR measurements were performed on a custom experimental platform built around an inverted microscope [Eclipse TE2000, Nikon, Melville, NY; Fig. 2(b)]. All experiments were performed at 37°C, controlled by means of an environmental chamber enclosing the microscope. The sensor was excited using a narrow-band LED with an emission maximum of 396 nm (Lumibright, Innovations in Optics, Woburn, MA) coupled to the epi-illumination port of the microscope. A diffuser was placed in front of the LED to produce uniform illumination of the field of view. The sensor was excited and emission photons were collected utilizing a $10\times$, 0.45 NA Plan Apochromat objective lens (Nikon) and a dichroic mirror (440 dclp, Chroma Corp., Bellows Falls, VT) mounted in the filter turret of the microscope. The emission signal was passed through one of two band-pass filters (BP595/70 and BP650/50, Omega Optical, Brattleboro, VT) for SiOEP and PtOEP emissions, respectively. These filters were mounted on a motorized filter wheel rotating in an infinity plane outside the microscope. Sensor emission images were collected using a cooled, electron multiplying, charge-coupled device camera (Cascade II 512, Photometrics, Tucson, AZ). The LED was operated in a pulsed mode synchronized to the camera exposure time to ensure that sensor excitation occurred only during image acquisition. Sensor data was captured every 5 sec with an exposure time of 20 ms per spectral channel. Alignment of well and lid arrays and the production of an air-tight seal between them, were accomplished using a high precision XYZ translation stage and a motorized rotation stage mounted on the microscope stage (Fig. 6).

Fig. 6

Bright-field micrograph of $3\times 3$ array of hermetically sealed microwells containing single cells.

Sensor intensity data was extracted from the images by defining threshold-based, annular or circular regions of interest (ROIs) encompassing each sensor’s area and calculating the average intensity value within each ROI. The average intensity values in both spectral detection channels were calculated using the following equation:

2.6.

Data Analysis

Statistical analyses of the data and data fitting procedures were performed using OriginPro software (v. 8, OriginLab, Northampton, MA). Data analysis software was written using LabView 8.6 (National Instruments, Austin, TX). We note that although all oxygen consumption data were analyzed after the experiments, the same type of analysis can easily be performed in real-time as well. We are currently modifying the software code to accomplish this task.

3.1.

Respirometry of Individual, Non-Interacting Cells

To evaluate system performance characteristics we measured OCRs of single cells from two different human epithelial cell lines, CP-A and CP-C, which were derived from patients having diagnoses of Barrett’s esophagus without dysplasia and with dysplasia, respectively.³⁰ Characteristic oxygen consumption curves for the two cell lines are presented in Fig. 7. We observed significant cell-to-cell differences in OCRs within each cell line and between the cell lines, with variations exceeding six-fold within a single cell line. Within a single experiment accommodating nine cells of a given cell line, we see marked OCR variations in all cell types. For example, the time needed to reach a 0% ${\mathrm{O}}_2$ in the microwell varies by about a factor of two for the CP-A cells [Fig. 7(a)]. Figure 7(b) shows responses of a subset of the CP-C cells that exhibited slow respiration kinetics. A significant portion of CP-C cells showed faster respiration kinetics, similar to those shown in Fig. 7(a) for CP-A cells. The fraction of slower-respiring CP-C cells [Fig. 7(b)] seem to exhibit less variability in OCR, as the times to approach 0% ${\mathrm{O}}_2$ vary only by factor of approximately 1.5. This implies that faster-respiring, more metabolically active cells may differ more from each other than less active, slow-metabolizing cells of the same type. The observed OCR heterogeneity within a cell type may be partially attributed to the use of non-synchronized cells in these experiments. Each cell’s phase in the cell cycle probably contributed to the variability in OCR in addition to intrinsic intercellular heterogeneity.

Fig. 7

Comparison of single-cell OCR results obtained with two different cell lines. All cells were treated identically and incubated for 16 to 24 h prior to OCR measurement. (a) OCR curves obtained with single cells of metaplastic human esophageal epithelial cell line CP-A. (b) OCR time courses of slowly respiring individual cells of dysplastic human esophageal epithelial cell line CP-C. All cells exhibit linear OCR kinetics. Significant cell-to-cell variations in OCR are demonstrated. Signal-to-noise ratio observed in OCR traces increases as ${\mathrm{O}}_2$ decreases because of stronger sensor emission intensity at low ${\mathrm{O}}_2$. Data in all panels smoothed with sliding five-point average filter.

All oxygen consumption kinetics exhibited linear behavior with a constant OCR in the oxygen concentration range between 7 and $\sim 0.2\mathrm{ppm}$. Below 0.2 to $0.1\mathrm{ppm}$, the OCR exhibits a non-linear dependence on ${\mathrm{O}}_2$ concentration (Fig. 8). In this study, we do not focus on oxygen consumption kinetics in this low range of ${\mathrm{O}}_2$. Because it is thought provoking, other studies are underway to address this finding in more detail. We exclude the possibility that the observed nonlinearity in the respiration curve in this ${\mathrm{O}}_2$ range is caused by changes in the sensor response as oxygen concentration decreases. The sensor calibration demonstrated that sensor emission intensity changes caused by differences in the amount of oxygen can be well described by the Stern-Volmer law, and do not exhibit any significant deviations as the oxygen concentration changes. Therefore, we attribute the observed nonlinear behavior at low ${\mathrm{O}}_2$ to real alterations in cell OCRs.

Fig. 8

Oxygen consumption in CP-C cells under severe hypoxia. Shows zoomed-in region of low ${\mathrm{O}}_2$ of the respiration time course of cell 5 in Fig. 7(b). At ${\mathrm{O}}_2$ levels $<0.5\mathrm{ppm}$ respiration behavior is well fit with exponential relation (solid curve).

To calculate OCR values for each of the studied cells we applied a linear fit to the oxygen consumption time course data above 0.2 ppm (Fig. 9). The chosen linear function might not be the most accurate: other, more complex models may exist that describe the observed behavior more appropriately. However, the SNR intrinsic to the current realization of the method limits the ability to resolve more details. The linear fit was chosen to provide a means for quantitative analysis and comparisons among cells within and between cell lines. We calculated the OCR values using the slopes of the linear regression fit and converting the values into oxygen concentration using the following equation:

Fig. 9

OCR data analysis. Linear regression fit (solid line) to experimental data. Lower panel shows residuals of fit.

Fig. 10

Summary of single-cell oxygen consumption rate (OCR) results. (a) Distribution of single-cell OCRs in CP-A and CP-C cells. Significant cell-to-cell variations in OCR within each cell line are observed. Broad distributions may result from cells being in different cell cycle phases and/or intrinsic cell-to-cell heterogeneity. Means of two distributions are not significantly different at $P=0.05$, but the presence of subpopulation of CP-A cells (but not CP-C cells) with OCRs between 2 and $5\mathrm{fmol}/(\min ·\mathrm{cell})$ suggests metabolic differences between cell lines. (b) Single (blue curve) and double (green curves, red curve—sum) Gaussian fits were applied to CP-A (b) and CP-C (c) OCR distributions. Fits show at least two subpopulations of cells within each cell type. Fit results are summarized in Table 1. The differences in the OCR distribution shapes between CP-A and CP-C cells demonstrate the capability of the method to distinguish and characterize groups of cells by resolving intercellular differences in OCR in individual cells, where conventional, bulk sample-based approaches would fail to do so.

Table 1

Summary of OCR results from CP-A and CP-C cell lines.

To better characterize the differences in OCR distribution between the CP-A and CP-C cells, we performed Gaussian fits on the OCR histograms. Although it may not represent the best fit to the data, the Gaussian model was chosen to reveal quantitative differences between the two types of cells. We applied two Gaussian peak functions to fit the OCR histograms of both cell types [Table 1; Fig. 10(b), 10(c)]. The fits show that at least two subpopulations exist among the CP-A and CP-C cells in terms of their respiration rates. CP-A cells exhibit one “slow” fraction with OCR values centered at $1.07\pm 0.08\mathrm{fmol}/(\min ·\mathrm{cell})$ and a second, “fast” subpopulation with an OCR of $3.1\pm 0.6\mathrm{fmol}/(\min ·\mathrm{cell})$; (the absolute error values indicated here and below represent the fit error). The fractional contributions of the slow and fast subpopulations are 59% and 41%, respectively. Use of the terms “fast” and “slow” in this context are relative and meant to reflect the comparative nature of the data analysis. The CP-C cells show less distinct differences in the two subpopulations compared with CP-A. The slow fraction of CP-C cells has an average OCR of $0.83\pm 0.04\mathrm{fmol}/(\min ·\mathrm{cell})$ with a fractional contribution of 38%, whereas the fast fraction is centered at $1.63\pm 0.27\mathrm{fmol}/(\min ·\mathrm{cell})$ with a fractional contribution of 62% of cells.

The observed OCR heterogeneity and the differences in single-cell OCRs between cell types confirm the need for single-cell studies. Ensemble-averaged OCR studies conducted on samples of hundreds of thousands or millions of cells, or mitochondria isolated from similar numbers of cells, revealed a substantially linear oxygen consumption profile for ${\mathrm{O}}_2$ in the range from 7 to 0.1 ppm, and a hyperbolic profile at oxygen levels less than 0.1 ppm.^33–35 These findings are in accord with the temporal characteristics of the average OCR behavior observed in this study. Because different cell types were used in the previous studies and this work, it seems that the observed common features of the OC kinetics are cell type-independent. Obviously, more studies on different cell types need to be conducted to confirm (or refute) this finding. Differences between OCR values observed at the single-cell level in this study and the bulk cell samples of previous reports may have two different origins. One stems simply from the fact that the cell types used in the previous and this study are different, thus making it probable that OCR values can differ because of differences in basal respiration rates. The second source of difference can be the averaging of millions of unsynchronized responses of individual cells inherent to bulk experiments. As evident from the OCR data presented in this study [Figs. 7 and 10(a)], unsynchronized individual cells exhibit significant variations in oxygen consumption rate. Even so, the average OCR values for CP-A and CP-C cells were found not to be statistically different. In terms of their OCRs, at least two subpopulations were found in the CP-A and CP-C cell groups. Thus, strong evidence is presented, which supports the assertion that averaging OCR data from $\sim {10}^5$ to ${10}^7$ cells results in obscuring details of respiration kinetics and the cells’ physiologic responses to oxygen concentrations. Details that may be obscured include the substantial cell-to-cell differences in OCR and the existence of subpopulations of cells with a differing metabolism.

In current system realization, the time needed to collect the data of 150 cells ranges from 1.5 to 3 days. We are working to increase the throughput by at least one order of magnitude to enable faster data collection.

3.2.

Effect of Homotypic Cell-Cell Interactions on Cell Oxygen Consumption

In the second part of this study, we focused on measurement of the effects of homotypic intercellular interactions on the OCRs of CP-A cells. We placed one, two, or three cells in each microwell using the custom cell manipulation platform (see Materials and Methods). All experiments were conducted using the same loading, incubation, and OCR measurement conditions as with single, non-interacting cells. Data obtained from the experiments with one cell per well were used as a reference for data from the experiments with two and three cells per well. Occasionally, the number of cells per well changed after the 16 to 24-h incubation period because of cell division and/or cell migration from the microwell. Therefore, prior to each experiment the number of cells in each well was counted using bright-field microscopy, and the data obtained from the wells were sorted accordingly. In most cases the cell morphology in wells containing more than one cell indicated that the cells were adhered to the bottom and walls of the microwell, and in some cases contacting one another [Fig. 2(a)].

Representative curves quantifying the kinetics of single and interacting cell respiration are depicted in Fig. 11. Each curve in the panels represents oxygen consumption kinetics of one [Fig. 11(a)], two [Fig. 11(b)], or three [Fig. 11(c)] cells. As expected, the OCR values increased with the number of cells per well. Nonetheless, some of the curves measured with one cell per well showed steeper slopes, i.e., faster respiration, compared with two cells per well [Fig. 11(a) cell 4, and curves in Fig. 11(b)]. It is unclear what could cause this significant increase in the OCR in an individual cell. It may be attributed to differences in the cell’s phase in the cell cycle. It is possible, for example, that the cell 4 curve shown in Fig. 11(a) represents a cell in late $G_2$ or M phase, where the energy demand could be elevated because of imminent or ongoing cell division. In general, the data presented in Fig. 11 indicates that there are significant cell-to-cell variations in oxygen consumption rates. The slopes of the oxygen concentration time course differ up to $\sim 6$-fold with one cell per well, $\sim 2$-fold in wells containing two cells, and only 1.1 to 1.5-fold with three cells per well. This apparent decrease in OCR variability as the number of cells per well increases can be explained as the result of averaging the oxygen consumption behavior of two or three cells. This finding emphasizes an important feature of the approach: averaging for even as few as two cells can markedly alter measurement results.

Fig. 11

Oxygen consumption curves measured with one (a), two (b), and three (c) CP-A cells per microwell. In general, because of averaging, the oxygen consumption kinetics show less well-to-well variation as the number of cells per well increases. $x$-axes are scaled differently in each plot.

To compare among experiments with different numbers of cells per microwell, we normalized the OCR values to the number of cells contained in a well (${\mathrm{OCR}}_{\mathrm{norm}}$, Table 2) to obtain respiration rates in femtomoles per minute per cell. This allows for a direct comparison of oxygen consumption kinetics among the experiments. The results are summarized in Fig. 12. Comparison with OCR values obtained in microwells with single occupancy shows that, on average, the respiration rate per cell in microwells with three cells increases 4.46 times that of a cell in a microwell by itself. This finding indicates that the cellular respiration rate is strongly affected by the presence of neighboring cells of the same type. Moreover, the results imply that the level of increase in cellular respiration depends on the number of interacting cells. Compared to a lone cell, we do not observe an increase in the OCR when a single interacting neighbor is introduced, whereas a significant increase can be seen when three cells are allowed to interact in a microwell. The observed nonlinear dependence of oxygen consumption on the number of interacting cells implies the existence of complex cellular mechanisms which are capable of up or down-regulation of respiration rates in response to intercellular interactions. Interestingly, respiration measurements at bulk cell levels ($\sim {5.10}^4$ cells, 80% confluency, data not shown) with both CP-A and CP-C cells showed OCR values similar to the average OCR obtained in single-cell experiments. Although more detailed studies are needed to address this fact in more detail, the decrease in the average OCR in bulk samples may be a result of cell growth inhibition at high confluency levels. We are conducting more thorough studies, which focus on the effects of population size on cell respiration rate.

Fig. 12

OCRs of interacting cells. Descriptive statistics plot of data obtained with non-interacting, two interacting, and three interacting CP-A cells. Shows nonlinear increase in OCR with three interacting cells. The box chart shows following statistical values: Open square, mean; solid line, median; upper and lower box lines, the 75th and 25th percentiles, respectively; upper and lower whiskers, the 95th and 5th percentiles, respectively; and x, maximal and minimal values.

Table 2

OCR measured with individual non-interacting and interacting CP-A cells.

Although the data set is small, it is difficult to determine whether the observed increases in respiration rate were caused by direct cell-to-cell contacts, signaling mediated by soluble extracellular ligands, or both. Because some cells were in intimate contact with one another [Fig. 2(a)], all three alternatives are possible.