Live-cell imaging is becoming a vital part of the research process, with advances made in the methodology and approach, taking researchers forward in terms of accuracy and efficiency.
For instance, due to advancements in super-resolution microscopy, techniques alongside the development of fluorophores and fluorescent probes, have enabled us to image cellular and molecular components beyond the limits of optical resolution from ~ 250 nm to about ~10 nm (1).
These microscopy techniques require fluorescent dyes, proteins, or photoswitchable probes, which all come with their own limitations (2). We know that immunofluorescence techniques require harsh methods of fixation and antigen retrieval that can modify the morphology of membranous organelles.
In addition, the current methods demand cumbersome protocols for antibody optimization and control experiments to avoid non-specific staining, which means the process is running at less than optimal efficiency. Furthermore, the use of fluorescently tagged proteins has many pitfalls, including reduction of transfection efficiency and abnormal localization and function of fused proteins.
Where live-cell imaging uses fluorescent vital stains, we are then limited by the problems of photobleaching and phototoxicity, rendering this approach unsuitable for time-lapse experiments. This has increased the urgency for developing improved, more viable techniques for label-free live-cell imaging.
Label-free imaging has several advantages over fluorescence-based imaging in instances where researchers are striving to gain insight into the behavior of living cells. These include:
- Cells are not damaged due to fixatives, phototoxicity, and protein overexpression
- The information captured from the cells is in its native state
- It is a cost-effective and time-efficient approach, as there is no need for expensive probes, pre-treatment, fixation, and staining procedures
- Time-lapse monitoring is continuous, meaning cellular behavior, processes and even subcellular organelles and macromolecules can be tracked in detail
- Imaging can be done under more controlled conditions in media or buffered saline solutions
- The same cells can further be used for endpoint assays, including immunohistochemistry and flow cytometry, reducing experimental setup and complexity
- Remote monitoring of cellular processes, cell quality assessment, and monitoring cell confluency is a viable option
- This approach is more suitable for high-throughput applications, such as drug screening, along with automated imaging and analysis. This has huge implications in clinical drug discovery studies, where capturing multi-parametric data across time points is vital to achieving accurate results
What are the optics of label-free imaging?
While the fluorescence imaging approach is based on excitation and emission of light by fluorescent probes, label-free imaging utilizes cells' inherent properties of contrast mechanisms, such as thickness and refractive index (RI) (Figure 1).
Figure 1 | Basis of how phase delay is used to create contrast image. Simplified from Kasprowicz et al, 2017.
The more prevalent label-free techniques rely on phase contrast (PC) and differential interference contrast (DIC), which employ optical setups in order to convert phase shifts caused by cells/intracellular features into changes in light wave amplitude. Both PC and DIC optics have their limitations, such as poor contrast and a marked lack of quantitative image segmentation.
Compare this with the modern, quantitative phase imaging (QPI) technique which utilizes modalities to quantify the extent of any phase delay introduced by the sample in the form of pixel level information. Here, the pixel intensity is determined by the thickness and refractive index of the sample, which is a measure of the biomolecular composition and organization (3). Figure 2 explains the difference in the optics between PC, DIC, and QPI techniques, and a comparison of images obtained by the three above techniques is depicted in Figure 3. QPI techniques are fast-evolving to provide vastly improved optical contrast and spatio-temporal information.
Figure 2 | Simplistic flowchart explaining the basic difference in optical measurement in PC, DIC, and QPI imaging techniques. Adapted from Kasprowicz et al, 2017.
Figure 3 | Representative images and line profile obtained from DIC, PC, and QPI images of A549 cells. Note enhanced contrast and spatial resolution obtained by QPI techniques. Scale bar 50µm. Modified from Kasprowicz et al, 2017.
Questions to ask when determining the requirements of the equipment
Label-free imaging can be deployed in various ways, in order to obtain required information depending on the question in hand. For instance, questions for consideration could include:
- How large is the cellular population you would like to image?
- What is the ideal field of view?
- How long will the experiment take?
Asking these questions or discussing them with your project team, will help determine the spatial and temporal information that can be extracted from the experimental data.
Label-free imaging has not just been deployed for biological assays utilizing compact brightfield microscopes, equipped with image analysis algorithms; label-free imaging can also be used to monitor the growth of cell cultures, allowing teams to receive notifications when the desired level of cell confluence is reached with the option to also automatically maintain cells in the optimal conditions for growth and maximum cell viability.
For researchers looking to get insight into the quality of their cell culture during routine cell expansion procedures, we recommend looking at the CytoSMART CytoSMART Lux2.
Once these requirements are defined, the next stage of planning an experiment utilizing label-free live-cell imaging is to consider the following important parameters:
The details of the images that can be obtained using a microscope depend on the field of view (FOV) and magnification of the imaging optics. These two parameters are captured in the field of view number: the diameter of the view field (in millimeters) decreases when an increase in magnification of the optical set-up occurs.
While the magnification and field of view are negatively correlated, it is, in fact, possible to achieve a large field of view while maintaining a relatively large magnification. With the use of modern plan-apochromats and other specialized flat-field objectives, the field of view can be enlarged to capture a greater range of specimen features. Such features are desirable for experiments that study a large surface area of a cell culture vessel, e.g. when studying cell migration using wound healing assays. In these experiments, the full length of wells is often used to apply the scratch (wound) into a monolayer of cells. Here the ideal FOV is the whole well of the cell culture vessel, which has typically not been easily achieved, but using the right equipment and approach this can be done successfully.
On the other hand, the overall magnification becomes crucial when imaging single cells (~10-40 μm) or organelles (~1-2 µm). Advanced optics coupled with improved image processing algorithms can provide high resolution and high-quality images from even sub-cellular organelles.
For time-lapse experiments, the rates at which the images are captured depend on the speed of the image acquisition system. Dynamic processes, such as cell trafficking, need higher acquisition speed, as compared to slower processes like cell division (ranging in hours) and cell differentiation (ranging in days).
3. Duration of the experiment:
Where an experiment is spread over several hours or days, the cell culture must be maintained in optimum conditions (i.e. temperature, CO2- levels, humidity). In such cases it is important to use microscopes that are fully compatible with incubator use or have the flexibility to utilize a controlled chamber for storing the specimen.
We wrote an article on the importance of the proper environmental control during a live-cell imaging experiment. Find that article here.
4. The need for automation:
When planning experiments that require simultaneous image acquisition from multiple specimens and multiple time points, one might consider the use of automated imaging set-ups in order to streamline the process and ensure accuracy throughout.
For researchers that want to obtain long-term time-lapse videos of their specimens in cell-based assays we recommend looking at the CytoSMART Omni.
Are you clear on the level of detail you will need to observe?
Label-free imaging facilitates observation and analysis through a range of specificity and detail, making this a scalable approach for your experiments. Leading researchers are already embracing this approach, as illustrated in the below cross-section of applications for label-free imaging. Depending on the biological question, the authors involved in the studies below have exploited the advantages of label-free imaging to extract relevant information, providing metrics ranging from cell population level to intracellular level.
PC and DIC optics have been successfully used to study cell population behavior like cell growth, viability assays, drug efficacy testing, wound repair, and gap closure assays (4, 5). In Blum et al, for instance, PC live-cell imaging was used to compare cell proliferation and wound closure rate in wild type (WT) and calretinin knockout rodent primary mesothelial cells (Figure 4) (5). The scratch area was imaged every 15 min by live-cell imaging and image analysis was done by Image J.
Figure 4 | Wound healing rate was measured as the time taken to repopulate the scratch area in WT and calretinin knockout rodent mesothelial cells in culture, in 72 hours. Image used for the representative purpose from Blum et al, 2015.
QPI-based imaging provides additional information, such as the dry mass of a group of cells, utilizing its unique phase metric properties. QPI-based live-cell imaging combines dry mass and RI-based measurements in order to accurately measure cell proliferation and wound closure rates (4, 6).
See an example of how whole-well image analysis can help in setting up reliable wound healing assays.
Individual cell data
Genetically identical cells can show heterogeneity, owing to differences in their proteome and transcriptome. It becomes important to assess cell-to-cell variability in experiments with any clinical outcome, such as the response of tumor cells to chemotherapeutic agents. Cell motility and migration studies are of significance in understanding immune regulation, embryogenesis, inflammation, and cancer, and in these instances, a live or real-time approach to cell time-lapse imaging can be deployed to monitor individual cell motility and proliferation (7).
Kasprowicz et al, discusses how single-cell trajectories were estimated using time-lapse imaging with QPI optics (3). Figure 5 shows the individual cell trajectories in untreated and staurosporine treated MDA-MB-231 cultures for a 72-hour duration. Images were acquired at 10X magnification at regular 10-minute intervals with trajectory analysis carried out using Cell Analysis Toolbox from Phasefocus. Dynamic phenotype metrics obtained with QPI imaging and analysis exhibit increased unidirectional motion in staurosporine-treated MDA-MB-231 cells.
Figure 5 | QPI time sequence images and trajectory tracking of individual MDA-MB-231 cells indicate an increase in unidirectional motion in staurosporine-treated cells. Scale bar 500 μm. Figure modified from Kasprowicz et al, 2017 for review purposes.
Are you interested in tracking individual cells? Here, we provide cellular metrics that can help you in determining the right strategy for image analysis.
Cell cycle and lineage
In vitro cell cycles and cell lineage tracing has huge implications in the studying and analysis of cancer and differentiation. Label-free imaging facilitates the long-term imaging of successive divisions or differentiation of individual cells without potentially altering native behavior. Both PC and QPI techniques have successfully been used to study mitosis and cell division (8, 9), with enhanced algorithms.
Zhang et al, characterized stem cell differentiation over a 15-day period, utilizing QPI microscopy and evanescent wave microscopy (10). Briefly, two light-emitting diodes (LEDs) of 660 nm were used for illuminating the sample from the top (QPI) and from the bottom for TIRM (total internal reflection microscopy). As 660 nm is in the long-wavelength range, it is less phototoxic enabling imaging for prolonged periods of time without much damage to cells. Metrics assessed from the data extrapolated via both of these techniques enabled label-free high-resolution imaging of stem cell differentiation status, as depicted in Figure 6. Quantitative assessment of cell size and morphology was done using inbuilt algorithms in Matlab (The Mathworks).
Figure 6 | QPI coupled with TIRM (total internal reflection microscopy) gives high-resolution information (single-cell lineage tracking) to track stem cell differentiation in a 15 day culture period. Scale bar 50 µm. Modified from Zhang et al, 2018 for representative purposes.
Modification of traditional DIC and QPI techniques has enabled visualization of subcellular organelles like lipid droplets, mitochondria, ER, and Golgi from label-free live cells. Nishimura et al, devised a modified DIC-based imaging system to visualize sub-cellular organelles in the brightfield. For successful imaging at this level of detail, powerful lenses are required so you must ensure that the system you choose is sophisticated and robust enough for the task in-hand and capable of accurately verifying the desired optics.
The authors in this example utilized a RM-DIC microscope, equipped with a modulating retardation unit in the illumination path, introducing a pair of shear direction changers to achieve phase distribution (PD). The PD images obtained were then converted to ePD images using Imaris Image Analysis software (BITPLANE) to obtain high-resolution organelle-level images. Figure 7 shows the corresponding images of ER, Golgi, and mitochondria obtained using phase distribution (modified DIC) microscopy.
Figure 7 | Phase distribution (PD-modified DIC) enables visualization of sub-cellular organelles in NIH3T3 cells in brightfield. ePD- pseudo color PD image obtained by image processing. GFP fused with localization signals for mitochondria, ER and Golgi was expressed in NIH3T3 cells and used to visualize the organelles. Images obtained with PD and fluorescence display comparable resolution and clarity of the image. Scale bar 10 μm. Image modified from Nishimura et al, 2019 for representative purposes.
What are the opportunities moving forward?
Ongoing advancements in optics and AI algorithms provide enormous scope for the development of label-free live-cell imaging techniques in the near future. Furthermore, the increasing demand for remote cell culture monitoring in controlled experimental setups such as hypoxia chambers or in BSL3/BSL4 facilities has led to the development of compact imaging setups with automated cloud-based analysis (Drug Discovery World Fall 2019).
Within the drug discovery area of research, the need for high-throughput, automated imaging systems will only increase, and it is imperative that technologies evolve in order to fully unlock the benefits of label-free live-cell imaging. CytoSMART, along with researchers and other equipment providers, will continue innovating and designing to lead on these developments, supporting innovative, accurate label-free live-cell imaging techniques and setups.
Learn about all CytoSMART imaging solutions here