Natural Selection in Unnatural Places
Managing evolutionary forces in the lab
Although the day-to-day life aboard the HMS Beagle was probably pretty banal, it was also one helluva ride in the grand scheme of things. Endless horizons inspire expansive thinking, and perhaps they helped Darwin piece together his observations into the theory of natural selection.
The double life of natural selection
Natural selection commands significant respect among scientists yet faces skepticism from much of the public. Surveys show that 87% of scientists (including non-biologists) accept evolution by natural selection, compared to just 32% of the general population(1). While scientific disagreements often focus on nuanced aspects, public dissent tends to reject the concept outright.
Darwin anticipated this controversy when he published On the Origin of Species, acknowledging its potential to clash with religious views. He wrote, “I see no good reasons why the views given in this volume should shock the religious views of anyone.”
At the same time, one wonders if Darwin wasn’t looking for a little shock value by saying, “Whilst man, however well-behaved, at best is but a monkey shaved!”
Natural selection provides a framework to understand the persistence of life in changing environments. To grasp its full implications, we must first explore how life itself emerges from the fundamental building blocks of the natural world.
Emergent properties: from atoms to life
Life does not arise in isolation but emerges from the interaction of simpler components. Physics explains the behavior of atoms and their basic components. But when atoms come together into molecules they exhibit behaviors fundamentally different than those of the constituent atoms. This emergent property is the basis of chemistry.
Chemistry explores molecules—their composition, structure, properties, and interactions. In complex systems, these interactions can create feedback loops and homeostatic mechanisms. However, only a unique subset of chemical systems is capable of self-reproduction—this is where biology begins.
Biological systems stand out for their ability to reproduce themselves and adapt to their environment. This adaptability hinges on variation. When environmental changes exceed a system’s tolerance, heritable variations ensures that some members of a species might survive and persist. Variation is an inherent necessity for the persistence life.
Natural selection in unnatural environments
The peppered moth is a classic example of how changes in the environment can change which variations are favored. Prior to the Industrial Revolution the most common variant of the peppered moth was mostly light with an appearance reminiscent of crushed black pepper sprinkled across. As more and more factories were built and operated in London, more and more soot entered the air and darkened surfaces of trees and buildings. This created quite a vulnerability for the peppered moth, as it’s appearance previously lent it camoflauge while it slept through the day.
However, there was a variant of the peppered moth that had appeared in the population earlier in the 1800s. This variant was dark, and as a result enjoyed excellent camoflauge as soot darkened the surfaces around it. It was noted in 1848 - years before Darwin published On the Origin of Species - that the coloration of the peppered moth population was changing to this dark appearance. Today you can still observe moths with both colorations, owing largely to the fact that air quality has significantly improved since the Industrial Revolution and there is no longer the same selective pressure against the lighter variant.
This natural adaptation of species to their environment presents an interesting parallel to modern laboratory work. Just as the peppered moth population shifted in response to environmental changes, cell lines in laboratories undergo their own form of selection pressure. The key difference? While natural selection typically enhances survival fitness, the inadvertent selection pressures in lab environments may actually work against experimental validity.
The biologist’s paradox: minimizing variability in the lab
While natural selection thrives on variability to enable adaptation, scientists often strive to reduce variability in their experiments to minimize costs and ensure reliable results. This creates a fundamental tension: we study biological systems shaped by variation while attempting to eliminate that very characteristic in our experimental design.
Ironically, the same selective pressures that Darwin described in nature are at work in our petri dishes. Cell lines that grow fastest or are most resistant to stress tend to dominate cultures over time - a form of artificial selection that can undermine experimental goals. This is particularly evident in the case of HeLa cells, which have proven so robust that they frequently overtake other cell lines(2).
Although their infinite replication capacity makes immortalized cell lines, such as HeLa cells, easy to work with(3), they also differ from primary cells in significant ways, including alterations in lipid metabolism, extracellular metabolites, and key signaling pathways(4).
Adding to the complexity, studies reveal that 5-46% of the cell lines in research labs are misidentified (2,5). Even cells obtained from originating labs are mislabeled 18% of the time(6).
To recap, this means that in the pursuit of sample homogeneity, scientists are working with cells that are different in ways both known and unknown, if they even know what cells they are actually working with.
All of this to work with systems that have evolved to develop variations over time.
The false economy of traditional monitoring
This is not a new problem, nor is it newly discovered. Biologists have been working with HeLa cells since 1951, and the issue with HeLa cells invading other cell types was published in 1968.
Of course there are methods for characterizing and validating cell lines, and over the years this process has gotten cheaper and easier. Yet most labs still don’t do it. As a Nature article on the topic noted, “Avoidance and denial behaviors are widespread,”(7).
Most labs rely on the observations of the scientists to notice when something seems off, and to then address it. In this paradigm the things scientists notice are cell morphology changes (to identify different cell types), perhaps an odor from the incubator (common if the cultures have been infected with bacteria or yeast), or noticing the media color is off (it’s usually pH sensitive). This does nothing to detect mycoplasma which is notorious for changing cell metabolism.
In the situations where a scientist detects an issue, the general response is to bleach all of the cells and thawing some from your stock. Yet if even originating labs are wrong 18% of the time, how well does that really work?
Workflows have been the persistent barrier to quality control
Why would a scientist be willing to work so hard with a constant risk of the work being foundational flawed? What underlies the avoidance and denial? Two things: misaligned incentives and workflow. The incentives are misaligned because throwing out your data also means throwing out your ability to publish the data, or to achieve the next landmark for the course of study. But this is only an issue if you identify a problem after it’s been around for a while. If you are regularly testing your cells, you will discover and address shifts in your cells before a body of data is collected. This is an workflow problem.
There are several workflows behind this dynamic. First you have the work it takes to maintain your cell lines. Commonly known as culturing your cells, the basic process involves growing cells in dishes, frequently changing the media, until there is a certain density of cells in the dish. At that point it’s time to passage your cells by ensuring they are fully detached from the dish and one another, then seeding cells into new plates so they can continue to grow.
Eventually you need to do your experiments. The next time you passage your cells you will distribute them into dishes according to the experimental design. You will then perform the experiment, then collect the cells and process the samples to observe your results. This process occurs independently from the culturing of your cells.
Routine cell verification has traditionally been costly, both in assay expenses and lost time for other tasks. This opportunity cost is the real heart of resistance to routine verification.
Automating cell culture creates opportunities for better quality control
Understanding this unintended natural selection in our labs is crucial because it points us toward better solutions. Rather than fighting against biological variation, modern automation allows us to monitor and manage it systematically - working with, rather than against, the fundamental properties of living systems.
When people think about automation, they often think about a direct replication of the steps they are taking. This is a missed opportunity, and one that fails to fully take advantage of the capabilities of automation.
For a crude analogy, consider the alarm clock. Pre-alarm clock, if you wanted to guarantee being awoken at a specific time you needed someone else who would already be awake to come and wake you. The solution to automating this was not a robot that taps on your shoulder, but instead a machine that beeps at the indicated time. When we use technology, we need to rethink the solution from the ground up. And so too with verifying cells in laboratories.
In this case, the answer is not to focus on the process of cell verification. Instead, it’s to implement a solution that manages the entirety of self-reproduction in the lab. This includes:
automating the process of cell culture
comprehensive sensor arrays
software algorithms that use the sensor data to manage the hardware
a process for frequent cell observation, including visual cell and media assessment (all dishes)
sampling for assessment of cell health, stress, and toxicity
AI-based tools that take in the accompanying data sets to uncover sources of genetic drift and design experiments to uncover opportunities for better management
In effect, you need a self-driving cell culture system. This sounds like a huge lift - far too large for the average lab. In fact, the lift is not as big as it sounds, and it’s far more accessible than you imagine.
The biggest lift is the process of automating your cell culture. You want equipment that matches the volume of cells your lab works with, and in automated systems it’s generally not as easy as adding another incubator (yet). But automating cell culture is worth the lift even outside of cell verification. Consistent culture management minimizes variability, reduces contamination risks, and frees scientists to focus on experiments.
The key is that if you’re committing to the lift of automating your cell culture (the first 4 bullet points), it’s a small extra effort to accomplish the last two. Or, put another way, by going to the effort of fully modernizing your lab, you gain access to a new frontier of biological research.
Automation's true value is to elevate scientific standards
Automating cell culture addresses the challenges of sample variability and misidentification while creating opportunities for deeper insight. By incorporating advanced sensor systems and AI-driven analysis, labs can identify and resolve issues with cell health and genetic drift early, ensuring greater consistency and accuracy in research. More important than streamlining routine tasks, these systems can improve experimental reliability and quality control, paving the way for more robust and reproducible biological research.
Automating cell culture addresses the challenges of sample variability and misidentification while creating opportunities for deeper insight. By incorporating advanced sensor systems with AI-driven analysis and experimental design, labs can identify and resolve issues with cell health and genetic drift early, ensuring greater consistency and accuracy in research. More important than streamlining routine tasks, these systems improve experimental reliability and quality control, paving the way for more robust and reproducible biological research. In a way, we've come full circle from Darwin's observations aboard the Beagle - just as he recognized natural selection as "a power incessantly ready for action," modern automation helps us harness and direct these same evolutionary forces in our laboratories, transforming what was once an experimental liability into a window for deeper biological understanding.
Investigation of Cross-Contamination and Misidentification of 278 Widely Used Tumor Cell Lines
There are a few ways to end up with an immortalized cell line.
There are way more examples than I’m going to cite, but for the sake of backing up my claims, here are publications for lipid metabolism, extracellular metabolites, and signaling pathways.
Cell line authentication: a necessity for reproducible biomedical research.
Widespread intraspecies cross-contamination of human tumor cell lines arising at source