A new book by Pulitzer-winning physician-scientist Siddhartha Mukherjee will be released from Simon & Schuster on October 25, 2022.
The Song of the Cell begins with the discovery of cells and of germ theory, featuring characters such as Louis Pasteur and Robert Koch, who brought the cell “into intimate contact with pathology and medicine.” This intercourse would transform biomedicine, leading to the insight that we can treat disease by thinking at the cellular level. The slightest rearrangement of sick cells might be the path toward alleviating suffering for the organism: eroding the cell walls of a bacterium while sparing our human cells; inventing a medium that coaxes sperm and egg to dance into cellular union for in vitro fertilization (IVF); designing molecular missiles that home to the receptors decorating the exterior of cancer cells; teaching adult skin cells to remember their embryonic state for regenerative medicines.
Mukherjee uses the bulk of the book to elucidate key cell types in the human body, along with their “connective relationships” that enable key organs and organ systems to function. This includes the immune system, the heart, the brain, and so on. Mukherjee’s distinctive style features compelling anecdotes and human stories that animate the scientific (and unscientific) processes that have led to our current state of understanding. In his chapter on neurons and the brain, for example, he integrates Santiago Ramon y Cajal’s meticulous black ink sketches of neurons into Mukherjee’s own personal encounter with clinical depression. In one lucid section, he interviews Dr. Helen Mayberg, a pioneering neurologist who takes seriously the descriptive power of her patients’ metaphors, as they suffer from “caves,” “holes,” “voids,” and “force fields” that render their lives gray. Dr. Mayberg aims to stimulate patients’ neuronal cells in a manner that brings back the color.
Beyond exposing the insight and inventiveness that has arisen out of cell-based thinking, it seems that Mukherjee’s bigger project is an epistemological one. The early chapters of The Song of the Cell continually hint at the potential for redefining the basic unit of biology as the cell rather than the gene. The choice to center biomedicine around cells is, above all, a conspicuous choice not to center it around genes (the subject of Mukherjee’s previous book, The Gene), because genes dominate popular science communication.
This choice of cells over genes is most welcome. Cells are alive. Genes are not. Letters—such as the As, Cs, Gs, and Ts that represent the nucleotides of DNA, which make up our genes—must be synthesized into a word or poem or song that offers a glimpse into deeper truths. A key idea embedded in this thinking is that of emergence. Whether in ancient myth or modern art, creation tends to be an emergent process, not a linearly coded script. The cell is our current best guess for the basic unit of life’s emergence, turning a finite set of chemical building blocks—nucleic acids, proteins, sugars, fats—into a replicative, evolving system for fighting stasis and entropy. The cell’s song is one for our times, for it is the song of biology’s emergence out of chemistry and physics, into the “frenetically active process” of homeostasis.
Re-centering our view of biology has practical consequences, too, for how we think about diagnosing and treating disease, and for inventing new medicines. Centering cells presents a challenge: which type of cell to place at the center? Rather than default to the apparent simplicity of DNA as a symbol because it represents the one master code for life, the tension in defining the diversity of cells—a mapping process still far from complete in cutting-edge biology laboratories—can help to create a more thoughtful library of cellular metaphors to shape both the practice and communication of biology.
Further, effective problem solving is often about operating at the right level, or the right scale. The cell feels like appropriate level at which to interrogate many of the diseases that ail us, because the senses that guide our own perceptions of sickness and health—the smoldering pain of inflammation, the tunnel vision of a migraine, the dizziness of a fluttering heart—are emergent.
This, unfortunately, is sort of where Mukherjee leaves the reader, under-exploring the consequences of a biology of emergence. Many practical and profound questions have to do with the ways that each scale of life feeds back on the others. In a tome on Cells and “the future human” I wished that Mukherjee had created more space for seeking the ways that cells will shape and be shaped by the future, of humanity and otherwise.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts.
For example, when discussing the regenerative power of pluripotent stem cells, Mukherjee raises the philosophical thought experiment of the Delphic boat, also known as the Ship of Theseus. The boat is made of many pieces of wood, each of which is replaced for repairs over the years, with the boat’s structure unchanged. Eventually none of the boat’s original wood remains: Is it the same boat?
Mukherjee raises the Delphic boat in one paragraph at the end of the chapter on stem cells, as a metaphor related to the possibility of stem cell-enabled regeneration in perpetuity. He does not follow any of the threads of potential answers. Given the current state of cellular engineering, about which Mukherjee is a world expert from his work as a physician-scientist, this book could have used an entire section dedicated to probing this question and, importantly, the ways this thought experiment falls apart.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts. Wholeness at any one of these scales of life—organelle, cell, organ, body, ecosystem—is what is at stake if we allow biological reductionism to assume away the relation between those scales.
In other words, Mukherjee succeeds in providing a masterful and compelling narrative of the lives of many of the cells that emerge to enliven us. Like his previous books, it is a worthwhile read for anyone curious about the role of cells in disease and in health. And yet, he fails to offer the broader context of The Song of the Cell.
As leading agronomist and essayist Wes Jackson has written, “The sequence of amino acids that is at home in the human cell, when produced inside the bacterial cell, does not fold quite right. Something about the E. coli internal environment affects the tertiary structure of the protein and makes it inactive. The whole in this case, the E. coli cell, affects the part—the newly made protein. Where is the priority of part now?” [1]
Beyond the ways that different kingdoms of life translate the same genetic code, the practical situation for humanity today relates to the ways that the different disciplines of modern life use values and culture to influence our genes, cells, bodies, and environment. It may be that humans will soon become a bit like the Delphic boat, infused with the buzz of fresh cells to repopulate different niches within our bodies, for healthier, longer lives. But in biology, as in writing, a mixed metaphor can cause something of a cacophony. For we are not boats with parts to be replaced piecemeal. And nor are whales, nor alpine forests, nor topsoil. Life isn’t a sum of parts, and neither is a song that rings true.
[1] Wes Jackson, "Visions and Assumptions," in Nature as Measure (p. 52-53).
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.