5 factors to consider when choosing a sustainable live-cell imaging system

Globally, we start to notice more and more consequences of climate change, resulting in the worldwide ambition to keep the global temperature increase below 2.0°C – or preferably below 1.5°C [1]. To make this ambition realistic, every sector will have to take action and decrease its environmental footprint. Although a life sciences laboratory may not be the first location that comes to mind for potential sustainable changes in daily practice, there are numerous alternatives to the current golden standards that can decrease a laboratory’s environmental footprint. This article highlights considerations for more sustainable live-cell imaging. Live-cell imaging enables researchers to determine not only whether, but also when and how certain cellular events occur in culture. Therefore, this technique is increasingly applied in cell research. The environmental footprint of live-cell imaging can be large, although more sustainable alternatives are also available within this experimental procedure. When looking for a live-cell imaging system, the following 5 factors can already increase sustainability with minimal effort:

Sustainability factors for live-cell imaging


1) Light source

Light source

Microscopic imaging is impossible without a decent light source. However, the currently applied halogen lamps and traditional lasers have low efficiency in light production [2, 3]: much of the consumed energy is converted into produced heat and therefore does not contribute to the imaging. Even before starting an imaging experiment, certain lasers require ample time to warm up [4], thereby consuming electricity that is not directly used for imaging. This warm-up time seriously affects the lifespan of the laser, and on top of that generally requires the laser to stay on during the intervals in between imaging, since the laser cannot warm up sufficiently fast when continuously turned on and off.

As an alternative for halogen lamps and lasers, LEDs can be used as a light source in microscopes as well. LEDs have a longer life span than halogen lamps [2] and higher energy efficiency: LEDs produce approximately 120 lumen/watt, versus approximately 20 lumen/watt for halogen lamps [2]. Since LEDs also do not need to warm up before being ready to image, they can be turned off during intervals in between imaging, and an imaging device can even go into sleep mode. Although the maximum power of an LED is often lower compared to a laser [5], the maximum light intensity of an LED is sufficient for most commonly-used stainings and probes [6]. Altogether, an LED is a more energy-efficient alternative light source for everyday microscopic imaging compared to halogen lamps and traditional lasers.


2) Connected computer

Connected computer

Nowadays a computer is connected to almost every microscope, to enable microscope control and data recording. These computers need to be running constantly during a live-cell imaging experiment – and consequently consume energy during the entire experiment. When a laptop is chosen for microscope control, it needs to be constantly connected to a power supply to prevent an empty battery during the experiment. Even when the battery is fully charged, this constant connection consumes electricity [7]. Also, the life span of the battery will be shortened by permanently connecting it to a power supply [8]. For most microscopy setups, one computer can control one microscope, so the number of running computers is equal to the number of microscopes. However, when multiple microscopes can be controlled by the same computer, this can drastically reduce the amount of consumed energy, and be a sustainable alternative. Many microscope systems are too complex to enable connecting multiple microscopes to the same computer, but the simpler systems may provide this sustainable option.


3) Data storage

Data strorage

There are roughly two approaches for data storage in live-cell imaging: either locally on the connected computer, or in a cloud-based environment. When purely comparing a local computer to a cloud-based system for exactly the same data storage, a cloud is consuming more electricity: the data needs to be transferred to a data center, where servers are running non-stop – and consuming electricity [9]. A local computer can be turned off at any moment when data does not need to be immediately accessible. Besides that, data stored in the cloud is not stored at one (physical) location: there are multiple back-ups to guarantee data preservation, which all occupy memory space at constantly running servers [10].

However, there are other factors in data storage, nuancing the difference between local and cloud-based storage. Users also have to back up data stored on their local computer, and often choose a cloud-based system and/or institute server system for this backup, nullifying the difference in energy consumption of running servers. Opposed to most cloud-based systems for individual users which store data even after a user has deleted it [11, 12], there are also cloud-based systems where data is stored only for the duration of a corresponding license [13]. The former allows restoration of data after accidentally deleting it but also causes the data to occupy (physical) memory space longer than required. The latter is therefore a more sustainable alternative.


4) Remote monitoring and analysis

Remote monitoring and analysis

In classical imaging setups, users need to visit the laboratory to check their recorded images on the computer connected to the microscope. However, if images can be accessed remotely, e.g., via a cloud-based system, unnecessary visits to the laboratory can be prevented. Particularly if traveling by car is reduced, this provides a gain in the carbon footprint. However, the monitoring itself can also be a more sustainable alternative to classical read-outs, since the live monitoring of experiments provides information of the same sample over time. With classical destructive read-outs, many more samples and therefore disposables and consumables are required for obtaining a time profile of results, reducing sustainability.

With the obtained time profiles from live monitoring, more directed follow-up experiments can be designed, since the most interesting and information-providing time points can be selected. This will also reduce the required disposables and consumables for properly answering a research question.

With data analyses for imaging becoming more and more complex over the years, the electricity consumption of computers performing the analyses has increased [14]. However, regularly updating an analysis algorithm can reduce the energy consumed during analyses [15]: if an algorithm is computationally more efficient – i.e., using fewer computational steps to get to a result – it also requires less time on the servers and therefore consumes less electricity. Cloud-based analysis algorithms can be more easily updated compared to local analyses, thereby constantly improving computational and energy efficiency.

5) System complexity

System complexity

Most commonly-used microscope systems consist of many complex components that need to be assembled properly for the system to function optimally. For live-cell imaging, the components for maintaining cells in an optimal environment (37°C and 5% CO2) also need to be included in the system [16]. Often these systems become so complex that they can only be assembled by one or more trained technicians, who need to travel to the laboratory where the system needs to be set up. Afterwards, extensive training needs to be provided to the users. This training is usually given by either the technicians who set up the system or another trainer from the supplier, who also needs to travel to the laboratory to facilitate thorough training. However, if a system is sufficiently straightforward to use that laboratory members can assemble it themselves and need minimal training before use e.g., via a video call, this can limit the number of large carbon footprint travels by supplier employees. Besides that, simpler systems will also contain less energy-consuming components.


CytoSMART devices and sustainability

By considering the 5 factors described above, the sustainability of a live-cell imaging setup can be easily increased. The CytoSMART devices for live-cell imaging perform well on these factors. The light sources of the devices are energy-efficient: all devices are equipped with LEDs and go into sleep mode when not actively used for 1 minute. Like most microscopes, the CytoSMART devices have to be connected to a computer for imaging. However, up to 4 devices can be connected to the same computer, reducing the consumed energy by the computer by 75% compared to systems with a computer per device. Data recorded with the CytoSMART devices is stored in the cloud, but only for the duration of the corresponding cloud license. All data storage and backup are thereby provided in one system, so no additional local data storage is required, whereas unused data no longer occupy server space after the expiration of the license. The cloud-based data storage also enables remote monitoring and analysis of experiments, where the analysis algorithms can be easily updated to versions with improved computational efficiency. All CytoSMART systems are described as ‘plug-and-play’, meaning that no trained experts are required for setting up and introducing a system: this can all be done with minimal remote assistance. Since the systems easily fit in an incubator and leave room for cell cultures next to it, no extra incubator or incubation box is required.


Concluding remarks

Altogether, performing life sciences experiments may not be the most sustainable activity per se, but more sustainable alternatives for the existing golden standards are available. For live-cell imaging, the factors described in this writing should be considered when looking for a more sustainable system, e.g., one of the CytoSMART imaging devices.

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