Hi Reader, in a few years no scientists will pipette anymore.
This is what I was told during a lab visit before I started studying many years ago.
While it hasn’t come true, and probably won’t within the next few years, many exciting innovations in automation exist.
Let’s go through the expected and unexpected aspects of how they can make labs greener:
Today's Lesson: Laboratory Automation
Upgrading processes and what it might yield
Number of the Day
Automated immunochemistry analyzers perform hundreds of tests per hour. Still, as Findeisen et al. determined, choosing the right unit can save up to 78.6% of hazardous waste. Moreover, the most efficient analyzer required just 8.5% of the cold storage volume compared to the least efficient model when assessed for 18 assays (including common measures such as ferritin, PTH, estradiol, and progesterone). What other savings potential does automation offer?
78.6
Fusing Automation and Sustainability
Lab automation is not just robotics.
It is an umbrella term encompassing instrumentation, software, system integration, and autonomous processing.
In essence, automation refers to several aspects of performing tasks with minimal human intervention.
Automated labs like those from Fraunhofer are becoming more common, especially for high-throughput approaches. On the right, I assembled some key features of automation to give you an impression of what the literature distinguishes.
As you surely know by now, I understand sustainability as an approach to optimize efficiency and effectiveness in order to save resources down the line.
Thus, I see in automation three main sustainability benefits: miniaturization, precision, and integration.
As discussed in Croxatto et al., we have to differentiate between modular and total laboratory approaches. The former automate a single (or a few) tasks, whereas the latter are designed to take over entire processes. While the former are very feasible for academic R&D, the more advanced options are more common in industry, such as diagnostics, compound screening, or quality control.
Let’s therefore transcend the environmental aspect of sustainability and see how we can save time, money, and resources while increasing precision when automation is implemented properly.
Miniaturization
Let’s cut straight to the point: through miniaturization, authors like Cain-Hom have shown that up to 50% of reagents can be saved.
They switched to an acoustic dispensing-assisted automated genotyping approach featuring 384-well plates reducing reaction mix volume from 20 µL to 3–5 µL (plus dead volume).
Please also note that smaller formats lead to less evaporation of samples - a factor contributing to data consistency and sample preservation. Auld and colleagues did a wonderful job discussing the ins and outs of plate properties and formats. Robots are simply more capable of working with 384- and 1536-well plate formats - with modern technology allowing processing at the nanoliter scale.
Importantly, they were able to cut processing time from over one hour to 11 minutes.
However, there are two other important aspects:
First, as the demand for high-throughput analysis of diagnostic and analytical samples is rising, smaller formats also mean less space usage. Think about Findeisen’s findings: with smaller systems, we avoid the need to build, equip, and maintain additional laboratory space.
Second, miniaturization can also mean reducing the number of analyses overall. Dreiman and colleagues suggested that using a machine-learning-assisted screening strategy could achieve a return rate of up to 90% even when screening only 50% of the collection. With each iteration, the AI-like model learns which targets are most promising to test.
Precision
Of course, the lower error rate of robots means less waste due to failed experiments.
But let’s dive into some more interesting aspects. First: standardization.
Predictive analytics can, to some extent, account for edge effects, which occur when plates or their wells show different behavior at the very edges. As robots pipette more precisely, we can expect overall fewer errors due to pipetting mistakes, such as those shown on the far right. Moreover, several systems include a centrifugation step that further alleviates these issues. Consult Auld and colleagues for more details.
We are all painfully aware of the low reproducibility of scientific data.
However, automated workflows tend to be easier to replicate, and movement patterns or shaking times are generally more uniform.
Moreover, we may observe a compounding effect, as the design of automated workflows requires precise instructions and can be more easily traced and reported afterward.
As shown here by 1lims and altexsoft, LIMS (Laboratory Information Management System) software platforms are used to manage laboratory samples, workflows, data, and reporting - making planning, optimization, and reporting much more stringent. Of note, Electronic Laboratory Notebooks (ELNs) may support these purposes as well.
Higher precision when it comes to volumes or immersion depths, and more accurate data acquisition, i.e., more standardized data also means less need for replication.
Fontana et al., provided a nice example as their detection was more consistent and precise - enhancing sensitivity and saving time.
This figure from Fontana et al. shows the subtle color differences in their cultures, which can be differentiated more effectively using digital analysis than by eye - both in terms of reliability and when it comes to counting.
Integration
We save resources and time when instruments combine tasks that would otherwise be separate for humans.
While machines are generally faster than humans, having preparation, detection, and analysis combined is particularly advantageous.
It saves a lot of time. And coming back to data quality, faster processing also helps protect our samples.
Croxatto and colleagues put together this excellent graphic demonstrating how a single instrument can prepare and analyze their cultures - tasks that not infrequently require scientists to move between rooms and start, calibrate, or turn off various instruments. Also, just consider that when time-differential analyses are conducted, data can be processed by the analyzer right away (and more uniform sample placement further facilitates this process).
Additionally, automatically integrated processes also mean that humans may have less contact with hazardous substances during handling and disposal.
Lastly, especially for laboratories with high throughput or academic spaces with high turnover, traceability is a key factor.
Automated systems allow for easier data organization and therefore reduce the chance of data loss and save time as well as resources by tracing samples or reagents in storage that might otherwise get lost.
Applying The Knowledge
Instruments with robotic integration that feature advanced software and AI-supported analysis save a great deal of time and resources.
They are faster and operate more precisely with much smaller volumes:
Turnaround time was reduced by 20–50% by Fontana and Croxatto et al.
Enhanced workflows included e.g., an increased yield of discrete colonies, thereby facilitating downstream analysis.
Plastic-waste savings are often encountered, for instance, Fungreduced the number of containers used for samples from 10,710 to 6,459 per month in their diagnostic workflow.
Click to enlarge. Fungoutlines where they identified opportunities for optimization. Interestingly, she also reports that frequent cracking of polystyrene aliquot tubes caused by the system’s gripper arm and inconsistent barcode recognition of water-resistant, shiny plastic labels led them to switch to polypropylene aliquot tubes and, in some cases, to paper labels. Polypropylene is generally recycled more often than polystyrene. A transition to automation can therefore create the momentum needed for such optimizations.
Of course, it is now up to you to think about how automation might support your workflows.
The main goal of automation is the reduction of inefficient and/or repetitive tasks (eventually reducing resource use).
However, take all these numbers with a grain of salt. Not all processes can be readily automated, and optimization is often required.
Moreover, automation may come with rather significant risks. Let’s talk about those next time.
How We Feel Today
References
Findeisen, P., et al., 2019. Cooled storage space and solid infectious waste production: results of a comparative study across six immunochemistry analysers. Clinica Chimica Acta, 493(Suppl 1), S517. doi:10.1016/j.cca.2019.03.1089.
Croxatto, A., et al., 2016. Laboratory automation in clinical bacteriology: what system to choose? Clinical Microbiology and Infection, 22(3), 217–235. doi:10.1016/j.cmi.2015.09.030.
Cain-Hom, C., et al., 2016. Mammalian genotyping using acoustic droplet ejection for enhanced data reproducibility, superior throughput, and minimized cross-contamination. Journal of Laboratory Automation, 21(1), 37–48. doi:10.1177/2211068215601637.
Dreiman, G.H.S., et al., 2021. Changing the HTS paradigm: AI-driven iterative screening for hit finding. SLAS Discovery, 26(2), 257–262. doi:10.1177/2472555220949495.
Fontana, C., et al., 2023. Laboratory automation in microbiology: impact on turnaround time of microbiological samples in COVID time. Diagnostics, 13(13), 2243. doi:10.3390/diagnostics13132243.
Croxatto, A., et al., 2015. Comparison of inoculation with the InoqulA and WASP automated systems with manual inoculation. Journal of Clinical Microbiology, 53(7), 2298–2307. doi:10.1128/JCM.03076-14.
Fung, A.W.S., 2025. Establishing sustainable quality improvement in the clinical laboratory: redesign of the total testing process and digital transformation of routine quality assurance activities. Clinical Biochemistry, 137, 110915. doi:10.1016/j.clinbiochem.2025.110915.
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