2D or 3D? How cell motility measurements are conserved across dimensions in vitro and translate in vivo

Abstract Cell motility is a critical aspect of several processes, such as wound healing and immunity; however, it is dysregulated in cancer. Current limitations of imaging tools make it difficult to study cell migration in vivo. To overcome this, and to identify drivers from the microenvironment that regulate cell migration, bioengineers have developed 2D (two‐dimensional) and 3D (three‐dimensional) tissue model systems in which to study cell motility in vitro, with the aim of mimicking elements of the environments in which cells move in vivo. However, there has been no systematic study to explicitly relate and compare cell motility measurements between these geometries or systems. Here, we provide such analysis on our own data, as well as across data in existing literature to understand whether, and which, metrics are conserved across systems. To our surprise, only one metric of cell movement on 2D surfaces significantly and positively correlates with cell migration in 3D environments (percent migrating cells), and cell invasion in 3D has a weak, negative correlation with glioblastoma invasion in vivo. Finally, to compare across complex model systems, in vivo data, and data from different labs, we suggest that groups report an effect size, a statistical tool that is most translatable across experiments and labs, when conducting experiments that affect cellular motility.

chronic inflammatory conditions, and delayed wound healing. 8,[14][15][16] Conversely, enhanced cell migration is a hallmark of cancer, with invasion of tumor cells correlating with poor patient prognosis. 17 In order to best understand aspects of cellular motility, such as cell migration and cell invasion, we and others have developed sophisticated and controllable in vitro systems. [18][19][20][21][22][23] For example, synthetic biomaterials designed to mimic the extracellular matrix (ECM) allow us to conduct experiments to better understand cell movement in 3D including interactions between cells and their ECM. These in vitro systems, coupled with live microscopy, have allowed us to see cells move in response to extracellular signals and genetic manipulations that would be impossible in vivo. These analyses have been reviewed most recently by Decaesteker et al. with the merits of each system described in detail. 24,25 Importantly, the jump to 3D systems creates a more physiologically relevant environment that now requires cells to not only feel and move around on surfaces, but to also squeeze, modify, and manipulate the environment around them. in vivo measurements of invasion and cellular movement is difficult, though has become possible through the use of intravital imaging with fluorescently labeled cells. 26,27 However, the use of 3D in vitro systems is still preferred not only due to the large cost associated with using animal models, but also due to their controllability, ease of implementation, and flexibility.
There are many challenges in analyzing the data collected on cellular motility and invasion with biomaterial-based systems. These include the diversity of assays, metrics, and analyses that result in difficulty in correlating results across platforms, stimuli, and labs. Most of the metrics used to analyze cellular invasion and motility have been developed in 2D and translated to 3D studies. We summarized the most commonly used metrics in Table 1, which include both continual live microscopy and endpoint imaging. We found cell migration reported on a population level, such as percent of cells invaded or migrating, or at a single cell level, such as migration speed or distance traveled. In this commentary, we describe the interrelation between these different motility measurements, the important differences in assays and reporting techniques used across the literature, and the potential predictive nature of in vitro assays to in vivo outcomes in a single model system.

| Common metrics for tumor cell motility often interrelate with one another
To begin to understand how cellular motility metrics may interrelate, we analyzed the correlations between outcomes for multiple glioma cell lines by calculating the Pearson's correlation coefficient r, where .1 ≤ |r| <.3 indicates weak correlation, .3 ≤ |r| < .5 indicates moderate correlation, and .5 ≤ |r| <1 indicates strong correlation. We summarize them in Table 1, which include percent invading cells, percent migrating cells, chemotactic index, speed, total, and net displacement.
Excluding percent invasion, which is a chamber-based endpoint assay, all other metrics mentioned are obtained from live, continuous microscopy. As a first case study, we compared live imaging and percent invasion data for several patient-derived glioma stem cell (GSC) lines, including G2, G34, G62, and G528 ( Figure 1, Figure S1). We first compared motility metrics assessed with live imaging to endpoint percent invasion and determined that no single metric significantly correlated with this endpoint metric (Figure 1a, p > .05). Although they are not statistically significant, there was a moderate correlation (.3 ≤ |r| <. 5) for chemotactic index (r = −.446, p = .199) and a strong correlation (0.5 ≤ |r| ≤1) for the speed (r = .742, p = .056). Next, we aimed to determine if there was a correlation between the percent of migrating cells in a total population and single cell metrics of motility ( Figure 1b  Chemotactic index X and Y coordinates Net distance/ total distance 0-1 1 Net distance X and Y coordinates Shortest distance between the initial and final position of the cell μm 3 Total distance X and Y coordinates Total distance traveled by the cell was also lower in 3D than in 2D, as has been commonly reported. [28][29][30][31][32] Observationally, the range of chemotactic indices was strongly correlated, though not statistically significant, between 2D and 3D ( Figure 2c, r = .948). When comparing the total and net displacement in 3D compared to 2D culture, there were weak correlations in between as well as statistically not significant. Thus, we were surprised to see that many metrics of individual cell motility did not correlate between 2D and 3D, though the total percent of migrating cells did.

| No obvious relationship between measurement time or cell density and cell migration quantification from the literature
The data in Figures 1 and 2 are a result of experiments performed in a single lab, and thus, potential confounding factors such as the culture medium, culture substrate, type and length of assay, and interpretation of data were largely controlled for. However, across the literature, cellular motility is examined not only via different metrics and assays, but also with varying experimental setup. Thus, we aimed to examine the variability in assay set up and its potential effects on outcomes through a careful literature search focused on several of the most widely examined cell lines in motility assays. We compiled data from a list of publications measuring motility in 2D and 3D platforms ( Figure 3 and Tables S1-S6) among widely used cell lines to extrapolate our findings to that beyond our own labs. We focused on studies of cell motility in 3D that reported % invasion (Figure 3a,b) and % migrating ( Figure 3c,d), and studies that reported % wound closure in 2D ( Figure 3e). We saw no significant correlation for the 3D motility outcomes with the two consistent experimental conditions reported (assay duration and cell density). In the case of wound healing assays, however, there was an unsurprising correlation between assay duration and percent of wound closure (r = .87, p < .01) (Figure 3c).
We found that biomaterial properties like pore size and composition were similar across studies, although concentrations of basement membrane extract (i.e., Matrigel ® ) used were often not reported (Tables S1-S2). Cell invasion outcomes from tissue culture insert assays were reported differently across publications and included total cell number, self-defined "invasion value," fold change, percent invasion, or images without quantitative metrics (Table S3). Assay readouts varied significantly between crystal violet, H&E staining, trypsinization prior to counting, or simply imaging counting, all at different time points (Tables S3-S5). In the case of invasion, attractants used in invasion assays were unique to each study (Table S6). Thus, we could not determine a correlation between the assay experimental setup and the cell migration-related outcomes. We were also unable to quantitatively evaluate all experimental design components (such as matrix concentration) within this small sample size of publications.
Similarly, when examining live imaging data in Collagen I matrices, another popular substrate for tumor cell motility assays, we saw a high degree of variability in metrics measured across 10 studies including percent migrating and cell speed ( Figure S4).

| In vivo invasion in glioma negatively correlates with 3D chemotactic index
One major stated goal of in vitro assays is to predict, or at least model,   when the Cohen's d is lower than 0.2, there is no effect. If the value is 0.2 ≤ |d| <0.5, there is a "small" effect, a "medium" effect if the value is 0.5 ≤ |d| <0.8, and a "large" effect when |d| ≥0.8 (Figure 5a). Thus, using this value, one can easily compare the effect of one treatment to another regardless of laboratory, experimental setup, or outcome measure to determine how universal findings are.

| Glioma motility in response to CXCL12
We examined motility of multiple patient-derived GSC lines in the presence of 100 nM of CXCL12 in 2D and 3D (Figure 5b) by reanalyzing our previously published data. 33 CXCL12 is a promigratory chemokine that has been implicated in glioma motility and invasion. 35 We quantified multiple outcomes with live cell tracking and found that the effect size varied based on the dimensionality. For some cell lines (G62) the effect size was nearly equal for percent motile cells when cells were stimulated in 2D or 3D and indicated that there was a small effect (<0.2) of the stimulation. For G2 and G528, the effect size varied but remained large (≥0.8) for both cell lines in both dimensions. Interestingly though, for G34, the effect in 2D was medium, but large in 3D, indicating that dimensionality may affect this cell line-specific response to CXCL12.

| Breast cancer motility in response to EGF and integrin inhibitors
To broaden the utility of effect size beyond glioma to breast cancer cell behavior, Figure 5c shows SkBr3 cells that were seeded on a bone-ECM functionalized surface and stimulated with epidermal growth factor (EGF) or inhibitors for integrin subunits β 1 and α 2 . 36  2D, but a medium effect in 3D. Our analysis highlights the utility of using the statistical tool effect size to determine its importance given its ability to span dimensionality and cell sources.

| DISCUSSION
In this analysis, we found that the diversity of invasion and motility assay measurement approaches, reporting tools, and responses all vary across labs (Figure 3 and Tables S1-S6). Although motility metrics have been studied in multiple contexts for decades, there is still not a consensus nor clarity in terms of the importance of each and the impact of each on outcomes in vivo. In cancer, this is particularly striking, as there is already a high level of heterogeneity in the disease itself, which is amplified as we move into complex in vitro models. One major impediment to the field's progress is the variability from lab to lab in the implementation and analysis of these experiments. First, we identified high variability in the assay setup. As illustrated in Table S1, concentrations of Matrigel ® used for invasion assays differed, and in some publications, were not reported. We know that the source and the lot of basement membrane extracts (like Matrigel ® ) can influence experiments alone, let alone the concentration. 37 Similarly, assay durations and cell densities differed across most publications using breast cancer cell lines (Tables S3-S5). Unsurprisingly, the assay duration correlated positively with degree of wound closure (Figure 3e). When we looked through how different publications quantified their assay outcomes, we noticed variable methods to count invasive cells from the bottoms of tissue culture inserts, including selection of immunocytological stain and/or fixation versus cellular detachment and counting.
Regardless, publications generally reported some final number, though this could be a percent, fold change, or total number of cells that prevented us from directly comparing their results as were able to do for our own experiments. A standardized metric that best conveys the raw data would allow to compare outcomes in a meaningful way across labs.
We propose effect size as a useful metric to understand how and if stimuli and inhibitors affect cell motility across geometries and labs.
For example, as seen on Figure 5b of the metrics in the two environments. 39 Next generation biomaterials are being developed that provide possible explanations of the key differences between 3D and 2D environments that drive the unique motility phenotypes, such as confinement 40,41 and porosity. 28 Many labs are quantifying cell invasion in vivo in order to potentially discover druggable targets to halt malignant cells from invading and metastasizing. 3D microenvironments have been lauded as "more physiologically relevant," but in our limited data set we show that there is no significant correlation (slight negative trend) between most motility metrics in 3D collagen/hyaluronan gels and invasion in vivo.
Live imaging data in vivo may reveal more information, but with at least this endpoint assay, we cannot predict in vivo "invasiveness" with in vitro invasion in glioma. This result is not altogether unsurprising in that the movement between dimensions and into a more complex system includes many changes to biophysical interactions. Thus, it is possible that our in vitro systems, even in 3D, do not have enough complexity to capture true in vivo behavior, such as additional cell-to-cell interactions, growth factors, cytokines, and specific integrin binding sites to the ECM. Further, it may be that we may never fully predict specific behaviors that translate in vivo, yet the information that we gain is still valuable for fundamental understanding of cell motility.

| Preparation of ECMs for SkBr3 migration experiments
Glass coverslips (15 mm and 18 mm diameter, Fisher Scientific, Agawam, MA) were functionalized with 10 g/L N,N-disuccinimidyl carbonate (Sigma-Aldrich) and 5% vol/vol diisopropylethylamine (Sigma-Aldrich), and ECM protein cocktails were then covalently bound to the glass coverslips through reactive amines: 5 μg/cm 2 of 99% Collagen I and 1% osteopontin. 36 Coverslips were incubated with proteins at room temperature for 3 hr, rinsed three times with PBS, and then incubated with 10 μg/cm 2 MA(PEG)24 (Thermo Scientific, Rockford, IL) for 2 hr. Coverslips were rinsed three times with PBS, epoxied to the plate (Devcon 5 min epoxy) and UV-sterilized prior to cell seeding.
For invasion studies from coverslips, cells were seeded on coverslips and then overlaid with a collagen gel as previous described. 36

| 3D invasion assays analysis
Invasion assay data for glioma cells was acquired from our previous publications where it was conducted as described. 33,42 Membranes were imaged at five non-overlapping locations and % invasion was calculated as an extrapolated cell count divided by the seeded cell count × 100. Data included in this publication were taken from our previous publications for RT2, G2, G34, G62, and G528. 33

| Tumor inoculation
Tumor images from previous publications were reanalyzed to determine the number of cells migrated per area beyond the tumor border.
Original experiments were approved by Institutional Animal Care and Use Committees as described in those publications. After importing raw images into ImageJ, cells were counted in four to five 0.49 mm 2 regions of the image. RT2 glioma cell line in rat 43 ; G2, G34, G528 GSCs in SCID mice 33 ; and G62 GSC in SCID mice. 44

| Invasion calculations from published data
Percent of invasion, and migration data were extracted with the WebPlotDigitizer v4.1 from the published work cited in Figure 3 and Tables S1-S6. Re-plotted data were used to calculate the percent of invasion based on the initial number of seeded cells.

| Effect size calculations
Effect size measures were performed between two independent groups following Cohen's d calculation:

| CONCLUSION
Current challenges in the field of cellular motility and invasion within biomaterial-based systems, including diversity of assays, metrics, and analyses, limit the translation of results across platforms and impede correlation between 2D, 3D and in vivo. Here, we summarize the most commonly used metrics to quantify cell motility, and describe the interrelation between these different motility measurements, the important differences in assays and reporting techniques used across the literature, and describe the potential contribution of in vitro predictions to in vivo outcomes. To our surprise, we found cell invasion in 3D has a weak negative correlation with invasion in a glioblastoma model in vivo. Given the variability we saw in reporting in the literature, and the inability to predict 3D or in vivo invasion from simpler 2D assays, we suggest that standardized metrics are needed. We recommend the use of effect size as a possible avenue that allows direct comparison between two different groups independent on dimensionality or stimulus. Given the rise of more physiological in vitro models that result in more complicated responses, this could be a first step to implement comparison of metrics across the field. Finally, standardizing motility metric outcomes could help bridge the gap between 2D, 3D in vitro systems and their translation to in vivo physiology.