One of the most difficult things to me about biology is defining what the fundamental unit is. Say we are to study cancer, and we want to know what was responsible for the development of a tumor. Do we think in terms of the whole person or the single transformed cell? Or what about the community of people that we live in and may have influenced our risk versus the DNA polymerase that made an error during DNA replication? I think that if we are to study biology, we have to choose the abstraction layer most relevant to our skills and have an intuition for which layer may be most impactful for addressing either our curiosity or the problem at hand.
At the current stage of science, it seems that single cells have become the most fashionable and technology forward approach for understanding biology and disease. After all, they can be relatively easily dissociated and the techniques that have been developed for exploring all kinds of features keep getting more and more sophisticated.
I think there is some thinking that if only we are able to improve capture efficiencies, assay all the -omes at once, improving annotation of cell properties 100x, we would be able to characterize and identify the root cause of anything. I think I subscribe to this belief, but I don’t think it is readily applicable to problems in the clinic. The reason is that the one variable you will never be able to control is time. Each patient presents to the clinic at a different stage of tumor development which is far more granular than the staging system used for clinical practice today. Because each person’s path to tumor development is different and because we can’t comprehensively sample, the learnings that we get from large scale single cell atlas studies, isn’t necessarily applicable to the 41 year old man with stage III pancreatic cancer sitting across from us in clinic.
We need an adaptive strategy. One where we can take non-invasive biopsies that are rich in features and can offer us a patient specific map of the vulnerabilities, causes, and next steps of each tumor. We can do this by recognizing that cells live in an ecosystem. The spatial microenvironment will give clues as to what the next steps for invasion are or how likely immune infiltration is to occur. Instead of abstracting the tumor in a cell by cell manner, perhaps we can consider modeling it as a sum of ecosystems. It could be a single clonal outgrowth, or multiple coexisting ones interacting with normal host.
Sampling representative ecosystems is likely quite difficult, but is something that is likely necessary to fully understand solid tumors. You need to understand beyond correlation which cells are abnormal and to what extent they are abnormal. Being able to utilize ecosystems to do intelligent differential expression beyond just tumor versus normal is accessible using spatial coordinates. It is a less invasive and less resource intensive way of accessing the spectacular precision of single cell atlas projects.
Ecosystems are the next logical step towards full tissue levels of understanding disease. New technologies are enabling both more precise and accurate scaling of abstraction. The scaling past molecular and cellular, to tissue, organ, and whole body level modeling that is actionable in the clinic will be the major driver of new treatments for disease. Accelerating the pace of scaling while nurturing proof of concepts at the basic and clinical levels will be the major objective of the 21st century. Meanwhile, the growing diversity of therapeutic modalities will complement our growing knowledge of disease drivers.
Having an intuition of how our understanding of biology is to progress has been tricky given the distractions of the over-optimistic but sometimes poorly understanding faction of “21st century is the year of biology” believers and the much more skeptical and modest “we don’t know anything” groups. At times it does seem a little random, like you are an arbitrage hunter without ownership of any given problem. But the framing of successively more complex abstraction as a method of progressing biological understanding is quite satisfying. It feels driven by the inevitability of progressively impressive tool development, with increasingly interesting applications and problems that are newly solvable. At the very least, if someone were to ask me for some framing on how progress in biology happens in the new age of biotechnology, I finally have a satisfying answer.