By Juan Rodríguez and Marco di Stefano (Members of the GENIGMA scientific team)
Have you ever wondered how scientists study cancer in the lab? How is it possible to reproduce a biological system in miniature to understand how the body works? What if you wanted to test the effectiveness of new drugs?
Laboratories around the world carry out their research using cell lines, also known as cell cultures. They have been around for a while, allowing scientists to accomplish several discoveries that would have been otherwise inconceivable. Thanks to cell lines, researchers made importance advances for the development of the polio vaccine, tested new chemotherapies, made important inroads in cell cloning processes, and played a key role in the development of in vitro fertilization. Some of this work went on to win Nobel prizes. The Covid-19 vaccines we use today were first developed and tested thanks to the cell cultures researchers had available at the time, allowing them to safely reproduce infections in a realistic environment.
As a research tool, cell cultures are unique in life sciences, having evolved leaps and bounds since the early 20th century. But you might be wondering… what does a video game like Genigma has to do with all this? To answer this question, we have to travel back in time.
The concept and origin of cell cultures
The concept of cell culture refers to the process by which cells are grown in a favourable artificial environment. After the cells have been isolated from living tissue, they are maintained under carefully controlled conditions. Advantages of using cell lines are manifold, for example:
- Allow for reproducing a cost-effective, biologically relevant environment for research
- Offer an alternative to animal experimentation in the lab
- Can be maintained for years (even frozen!)
- Respond to drugs and treatments similarly to body cells
The early 20th century was the time when the basic principles for plant and animal cell cultures in vitro were developed. The formulation of cellular theory in the late 19th century formed the basis for biological research, with three main hypotheses:
- All living organisms are composed of one or more cells
- The cell is the basic unit of structure and organization in organisms
- Cells arise from pre-existing cells
German embryologist Wilhelm Roux demonstrated in 1885 that it is possible to maintain living cells outside of the body in saline solution for a few days. He observed that neural cells of chicken embryos cells continued to “work” outside the body.
Since then, the process of generating cell cultures has continued developing, with the first cell line – the “L929” cell line – being established by Earle in 1948, and which is still around today! This cell line was derived from subcutaneous mouse tissue and displayed quite different morphology from the origin of tissue.
How cell lines are grown in a lab
To establish a new cell line, researchers first generate a primary culture, obtained directly from the tissues or organs. Cells grow until they occupy the full surface of a culture plate, and then stop growing. Then, they have to be subcultured (passaged) to a new plate. After this step, the process starts all over again and from now on it can be considered a cell culture or cell line.
The subculture technique allowed researchers to obtain cell lines from primary cultures, analogous to what we do with the sourdough for the bread or the kefir yeast for yoghurt. Primary cell cultures are mainly initiated from normal or malignant adult and/or embryonic tissues. For example, cancer cells from a biopsy of a cancer patient can be grown in a plate and a cell line can be established.
The cell lines established from normal tissues display finite growth. In contrast, cell lines obtained from cancerous tissues proliferate indefinitely (after all, that is the natural trait of cancer). However, normal cells can also be immortalized using routine laboratory procedures, meaning that under particular optimal conditions, they can reproduce forever.
The limitations of cell lines
Different cell lines are commonly used in different studies, but the use of cell lines also has some disadvantages and limitation.
One disadvantage is that the number of genetic aberrations in cell lines increase over time. Another is that a cell line’s response toward a drug might be not 100% equal to an actual patient response. After all, the cell culture environment in a plastic plate is obviously different from that of the original tumour in the body. Due to culture conditions, natural properties of the tumour or tissue can be lost, altering the tumour’s potential responses to treatments.
Cross‐contamination of cell cultures with another cell line is another particularly important issue. In a laboratory working with several cell lines, cross-contamination of cells can happen. When you think you are observing a certain phenomenon in a cell line, it could be that it has been contaminated with a different one.
Bacterial infections are another problem that can change a culture’s properties and ruin the line. Another problem is that not all types of tumours can be turned into cell lines, which makes some cancer research biased towards the use of easy to grow cell lines.
Unfortunately, we cannot solve all of the problems above. Still, the advantages outweigh the disadvantages.
The importance of the Genigma challenge
Genetics is what makes a cell behave and respond the way it does to treatments and stimuli. Everything is coded into a cell’s DNA; when to replicate, when to die, when to activate gene programs and react to an external cue.
Every cell comes from another cell. As cells are immortalized in culture, they have to fully copy their own genetic material once per division, a phenomenon that occurs so much in totality that it will likely give rise to copy-write errors.
Imagine that you have to manually copy the same page of a book every day for years and years. Eventually, small typos can be made, pass unnoticed, propagating and amplifying the errors. In a few years your page may not resemble anything like the original anymore.
You can easily imagine what may happen to the genetic material of a cell after decades in culture. We know that these errors are everywhere in the cell lines! In some cases, even full chromosomes have been duplicated (image). For cancer cell lines this is particularly pronounced, as the disease is characterised by processes where the cell loses control, parts of the genome are rearranged, duplicated or deleted, either as a cause or consequence of the disease.
When researchers work with cancer cell lines, they interpret their discoveries using a genomic map, which tells them, among other things, where the genes they have studied are located in the cell’s DNA. This map is what is known as a genome reference sequence.
For most studies, researchers use a canonical human genome reference sequence to navigate through the genomic landscapes of the cells and interpret their results. These maps, in principle, should be free of these aforementioned errors, but in reality, using these maps would be like navigating the streets of a city using a 150-year-old map.
Using Barcelona as an example, we’d recognise the sea and the mountain that border the city, but many neighborhoods or streets would have been created, expanded, or even bulldozed in that period of time. The map is no longer useful.
This is exactly what we want to do in Genigma. How can we build a tailored, precise genomic map of the most commonly-used cancer cell lines, reflecting all these changes in the genetic landscape of the cancer cell lines?
And how exactly do we want to do this? With the help of three-dimensional genomics (you can read more about it in our dedicated blog entry) and the help of powerful citizen science, in which we will use your brain computing power to solve some of the most complex and exciting problems in biology!
Humans vs. machines: ‘herd intelligence’ vs. ‘artificial intelligence’
Machines and computer algorithms can be very helpful to solve biological problems like the one we are tackling in Genigma. However, they have their limitations and cannot return all the accuracy such a daunting task would require.
For example, take a look at the image below. How quickly can you spot dogs?
It should be immediate obviously to most people! But what are the unique patterns that you are spotting in a dog’s face that your brain recognises as a dog’s face? Expressing that in words is no simple task. For example, muffins have blueberries or chocolate chunks that could be confused for dog faces with dark eyes and a nose, but something in your brain tells you that the chunks in muffins are not in an anatomical position. Similarly, dog faces are ight and brown but so is the dough in muffins.
If you had to build an automatic computer program to perform this task, you would have to introduce the concept of “dog” to the algorithm. It is extremely costly to accurately teach this concept to a computer program, with all that it implies. There are way too many concepts, both tangible and abstract, consciously or subconsciously, that our brain processes before reaching an answer.
So, how can we describe a set of complex concepts to a machine that make it return the correct answer in the correct context? This is one of the challenges of using current algorithms when we use them to generate reference genomic sequences. They can perform well, fast and accurate at first, but then at a more fine-grained level they may get easily confused, returning imprecise results. On the contrary, our brains can process a limited amount of information per time unit, while computers can work much faster. However, the brain tends to perform better when faced with fine scale problems.
For Genigma, we asked – what if we could split a huge problem in a thousand small pieces and then use the computing power of a thousand brains to get the solution for each of them?
Brains can also be prone to error, but collectively they are powerful. While algorithms will always make the same systematic errors when facing a same challenge, different human brains are less likely to make the same mistakes in the same places. This herd intelligence is exactly what we want to exploit with Genigma, in order to reconstruct this precious valuable cancer genome sequences to help researchers develop their therapeutic strategies guided with a more precise genomic map.