Research Area: Cell Biology

A more precise way to edit the genome

MIT researchers have dramatically lowered the error rate of prime editing, a technique that holds potential for treating many genetic disorders.

Anne Trafton | MIT News
September 17, 2025

A genome-editing technique known as prime editing holds potential for treating many diseases by transforming faulty genes into functional ones. However, the process carries a small chance of inserting errors that could be harmful.

MIT researchers have now found a way to dramatically lower the error rate of prime editing, using modified versions of the proteins involved in the process. This advance could make it easier to develop gene therapy treatments for a variety of diseases, the researchers say.

“This paper outlines a new approach to doing gene editing that doesn’t complicate the delivery system and doesn’t add additional steps, but results in a much more precise edit with fewer unwanted mutations,” says Phillip Sharp, an MIT Institute Professor Emeritus, a member of MIT’s Koch Institute for Integrative Cancer Research, and one of the senior authors of the new study.

With their new strategy, the MIT team was able to improve the error rate of prime editors from about one error in seven edits to one in 101 for the most-used editing mode, or from one error in 122 edits to one in 543 for a high-precision mode.

“For any drug, what you want is something that is effective, but with as few side effects as possible,” says Robert Langer, the David H. Koch Institute Professor at MIT, a member of the Koch Institute, and one of the senior authors of the new study. “For any disease where you might do genome editing, I would think this would ultimately be a safer, better way of doing it.”

Koch Institute research scientist Vikash Chauhan is the lead author of the paper, which appears today in Nature.

The potential for error

The earliest forms of gene therapy, first tested in the 1990s, involved delivering new genes carried by viruses. Subsequently, gene-editing techniques that use enzymes such as zinc finger nucleases to correct genes were developed. These nucleases are difficult to engineer, however, so adapting them to target different DNA sequences is a very laborious process.

Many years later, the CRISPR genome-editing system was discovered in bacteria, offering scientists a potentially much easier way to edit the genome. The CRISPR system consists of an enzyme called Cas9 that can cut double-stranded DNA at a particular spot, along with a guide RNA that tells Cas9 where to cut. Researchers have adapted this approach to cut out faulty gene sequences or to insert new ones, following an RNA template.

In 2019, researchers at the Broad Institute of MIT and Harvard reported the development of prime editing: a new system, based on CRISPR, that is more precise and has fewer off-target effects. A recent study reported that prime editors were successfully used to treat a patient with chronic granulomatous disease (CGD), a rare genetic disease that affects white blood cells.

“In principle, this technology could eventually be used to address many hundreds of genetic diseases by correcting small mutations directly in cells and tissues,” Chauhan says.

One of the advantages of prime editing is that it doesn’t require making a double-stranded cut in the target DNA. Instead, it uses a modified version of Cas9 that cuts just one of the complementary strands, opening up a flap where a new sequence can be inserted. A guide RNA delivered along with the prime editor serves as the template for the new sequence.

Once the new sequence has been copied, however, it must compete with the old DNA strand to be incorporated into the genome. If the old strand outcompetes the new one, the extra flap of new DNA hanging off may accidentally get incorporated somewhere else, giving rise to errors.

Many of these errors might be relatively harmless, but it’s possible that some could eventually lead to tumor development or other complications. With the most recent version of prime editors, this error rate ranges from one per seven edits to one per 121 edits for different editing modes.

“The technologies we have now are really a lot better than earlier gene therapy tools, but there’s always a chance for these unintended consequences,” Chauhan says.

Precise editing

To reduce those error rates, the MIT team decided to take advantage of a phenomenon they had observed in a 2023 study. In that paper, they found that while Cas9 usually cuts in the same DNA location every time, some mutated versions of the protein show a relaxation of those constraints. Instead of always cutting the same location, those Cas9 proteins would sometimes make their cut one or two bases further along the DNA sequence.

This relaxation, the researchers discovered, makes the old DNA strands less stable, so they get degraded, making it easier for the new strands to be incorporated without introducing any errors.

In the new study, the researchers were able to identify Cas9 mutations that dropped the error rate to 1/20th its original value. Then, by combining pairs of those mutations, they created a Cas9 editor that lowered the error rate even further, to 1/36th the original amount.

To make the editors even more accurate, the researchers incorporated their new Cas9 proteins into a prime editing system that has an RNA binding protein that stabilizes the ends of the RNA template more efficiently. This final editor, which the researchers call vPE, had an error rate just 1/60th of the original, ranging from one in 101 edits to one in 543 edits for different editing modes. These tests were performed in mouse and human cells.

The MIT team is now working on further improving the efficiency of prime editors, through further modifications of Cas9 and the RNA template. They are also working on ways to deliver the editors to specific tissues of the body, which is a longstanding challenge in gene therapy.

They also hope that other labs will begin using the new prime editing approach in their research studies. Prime editors are commonly used to explore many different questions, including how tissues develop, how populations of cancer cells evolve, and how cells respond to drug treatment.

“Genome editors are used extensively in research labs,” Chauhan says. “So the therapeutic aspect is exciting, but we are really excited to see how people start to integrate our editors into their research workflows.”

The research was funded by the Life Sciences Research Foundation, the National Institute of Biomedical Imaging and Bioengineering, the National Cancer Institute, and the Koch Institute Support (core) Grant from the National Cancer Institute.

Little picture, large revelations

A summer intensive using microscopy to study a unique type of yeast was a dream come true for BSG-MSRP-Bio student Adryanne Gonzalez.

Lillian Eden | Department of Biology
September 11, 2025

For Adryanne Gonzalez, studying yeast using microscopy at MIT this summer has been a dream come true. 

“Whatever world we’re living in, there’s an even smaller one,” Gonzalez says. “Knowing and understanding the smaller one can help us learn about the bigger stuff, and I think that’s so fascinating.” 

Gonzalez was part of the Bernard S. and Sophie G. Gould MIT Summer Research Program in Biology, working in the Lew Lab this summer. The program offers talented undergraduates from institutions with limited research opportunities at their home institutions the chance to spend 10 weeks at MIT, where they gain experience, hone skills, and create the types of connections with potential collaborators and future colleagues that are critical for success in academia. 

Gonzalez was so excited about the opportunity that she didn’t apply for any other summer programs.  

“I really wanted to work on becoming more independent in the lab, and this program was research-intensive, and you get to lead your own project,” she says. “It was this or nothing.”

two people standing at a bench in front of a computer
Adryanne Gonzalez, right, with her mentor, Lew Lab graduate student Clara Fikry, left. Gonzalez spent the summer studying Aureobasidium pullulans, a type of yeast that produces large, root-like networks. Photo credit: Mandana Sassanfar/MIT Department of Biology

The fun of science & the rigors of mentoring

The Lew Lab works with two different specimens: a model baker’s yeast that multiplies by producing a round growth called a bud that eventually separates into a separate, daughter cell; and Aureobasidium pullulans, which is unusual because it can create multiple buds at the same time, and can also spread in large networks of branching, rootlike growths called hyphae. A. pullulans is an emerging model system, meaning that researchers are still defining what normal growth and behavior is for the fungus, like how it senses and responds to obstacles, and how resources and molecular machinery are allocated to its branching structures.  

“I’m really interested in all the diversity of biology that we don’t get to study if we’re only focused on the model species,” says Clara Fikry, a graduate student in the Lew Lab and Gonzalez’s mentor for the summer. 

On the mentoring side, Fikry learned how to balance providing a rigorous workload while not overwhelming her mentee with information. 

“Science should be fun,” Fikry says. “The goal of this isn’t to produce as much data as possible; it’s to learn what the process of science is like.”

Although her day-to-day work was with Fikry, Gonzalez also received guidance from Daniel Lew himself. For example, his advice was invaluable for honing a draft of her research statement for potential graduate school applications, which she’d previously written as part of a class assignment.

“It was an assignment where I needed to hit a page count, and he pointed out that I kind of wrote the same thing three times in the first paragraph,” she shares with a laugh. He helped her understand that “when you’re writing something professionally, you want your writing to be concise and understandable to a broad spectrum of readers.” 

Life in the cohort

The BSG-MSRP-Bio program gives undergraduate students a taste of what the day-to-day life of graduate school might feel like, from balancing one’s workload and reading research papers to learning new techniques and troubleshooting when experiments don’t go as planned. Gonzalez recalls that the application process felt very “adult” and “professional” because she was responsible for reaching out to the faculty member of the lab she was interested in on her own behalf, rather than going through a program intermediary. 

Gonzalez is one of just three students from Massachusetts participating in the program this year—the program draws students from across the globe to study at MIT. 

Every student also arrives with different levels of experience, from Gonzalez, who can only work in a lab during the school year about once a week, to Calo Lab student Adriana Camacho-Badillo, who is in her third consecutive summer in the program, and continuing work on a project she began last year.

“We’re all different levels of novice, and we’re coming together, and we’re all really excited about research,” Gonzalez says.

Gonzalez is a Gould Fellow, supported at MIT through the generous donations of Mike Gould and Sara Moss. The program funding was initiated in 2015 to honor the memory of Gould’s parents, Bernard S. and Sophie G. Gould. Gould and Moss take the time to come to campus and meet the students they’re supporting every year. 

“You don’t often get to meet the person that’s helping you,” Gonzalez said. “They were so warm and welcoming, and at the end, when they were giving everyone a nice, firm handshake, Mike Gould said, ‘Make sure you keep going. Don’t give up,’ which was so sweet.” 

Gonzalez is also supported by Cedar Tree, a Boston-based family foundation that primarily funds local environmental initiatives. In the interest of building a pipeline for future scientists with potential interest in the environmental sciences and beyond, Cedar Tree recently established a grant program for local high school and undergraduate students pursuing STEM research and training opportunities. 

Gonzalez discusses her summer research with attendees of the poster session that serves as the culmination of the 10-week summer research intensive for talented non-MIT undergraduate students from around the world. Photo credit: Lillian Eden/MIT Department of Biology.

Preparing for the future

The BSG-MSRP-Bio program culminates with a lively poster session where students present their summer projects to the MIT community—the first time some students are presenting their data to the public in that format.

Although the program is aimed at students who foresee a career in academia, the majority of students who participate are uncertain about the specific field, organism, or process they’ll eventually want to study during a PhD program. For Gonzalez, the program has helped her feel more prepared for the potential rigors of academic research.

“I think the hardest thing about this program is convincing yourself to apply,” she says. “Don’t let that hinder you from exploring opportunities that may seem out of reach.” 

Inflammation jolts “sleeping” cancer cells awake, enabling them to multiply again

A paper from the Weinberg Lab indicates that inflammation may be a factor in how metastatic cancer cells, those that have broken away from the original tumor, can erupt into a frenzy of growth and division months, years, or decades after initial treatment, seeding new, life-threatening tumors.

Shafaq Zia | Whitehead Institute
September 3, 2025

This migration of cancer cells, called metastasis, is especially common in breast cancer. For many patients, the disease can return months—or even decades—after initial treatment, this time in an entirely different organ.

Whitehead Institute Founding Member Robert Weinberg, also the Daniel K. Ludwig Professor for Cancer Research at Massachusetts Institute of Technology (MIT), has spent decades unraveling the complex biology of metastasis and pursuing research that could improve survival rates among patients with metastatic breast cancer—or prevent metastasis altogether.

In their latest study, Weinberg, postdoctoral fellow Jingwei Zhang, and colleagues ask a critical question: what causes these dormant cancer cells to erupt into a frenzy of growth and division? The group’s findings, published Sept. 1 in The Proceedings of the National Academy of Sciences (PNAS), point to a unique culprit.

This awakening of dormant cancer cells, they’ve discovered, isn’t a spontaneous process. Instead, the wake-up call comes from the inflamed tissue surrounding the cells. One trigger for this inflammation is bleomycin, a common chemotherapy drug that can scar and thicken lung tissue.

“The inflammation jolts the dormant cancer cells awake,” Weinberg says. “Once awakened, they start multiplying again, seeding new life-threatening tumors in the body.”

Decoding metastasis

There’s a lot that scientists still don’t know about metastasis, but this much is clear: cancer cells must undergo a long and arduous journey to achieve it. The first step is to break away from their neighbors within the original tumor.

Normally, cells stick to one another using surface proteins that act as molecular “velcro” but some cancer cells can acquire genetic changes that disrupt the production of these proteins and make them more mobile and invasive, allowing them to detach from the parent tumor.

Once detached, they can penetrate blood vessels and lymphatic channels, which act as highways to distant organs.

While most cancer cells die at some point during this journey, a few persist. These cells exit the bloodstream and invade different tissues—lungs, liver, bone, and even the brain—to give birth to new, often more aggressive tumors.

“Almost 90% of cancer-related deaths occur not from the original tumor but when cancer cells spread to other parts of the body,” says Weinberg. “This is why it’s so important to understand how these ‘sleeping’ cancer cells can wake up and start growing again.”

Setting up shop in new tissue comes with changes in surroundings—the “tumor microenvironment”—to which the cancer cells may not be well-suited. These cells face constant threats, including detection and attack by the immune system.

To survive, they often enter a protective state of dormancy that puts a pause on growth and division. This dormant state also makes them resistant to conventional cancer treatments, which often target rapidly dividing cells.

To investigate what makes this dormancy reversible months or years down the line, researchers in the Weinberg Lab injected human breast cancer cells into mice. These cancer cells were modified to produce a fluorescent protein, allowing the scientists to track their behavior in the body.

The group then focused on cancer cells that had lodged themselves in the lung tissue. By examining them for specific proteins—Ki67, ITGB4 and p63—that act as markers of cell activity and state, the researchers were able to confirm that these cells were in a non-dividing, dormant state.

Previous work from the Weinberg Lab had shown that inflammation in organ tissue can provoke dormant breast cancer cells to start growing again. In this study, the team tested bleomycin—a chemotherapy drug known to cause lung inflammation—that can be given to patients after surgery to lower the risk of cancer recurrence.

The researchers found that lung inflammation from bleomycin was sufficient to trigger the growth of large lung cancer colonies in treated mice—and to shift the character of these once dormant cells to those that are more invasive and mobile.

Zeroing in on the tumor microenvironment, the team identified a type of immune cells, called M2 macrophages, as drivers of this process. These macrophages release molecules called epidermal growth factor receptor (EGFR) ligands, which bind to receptors on the surface of dormant cancer cells. This activates a cascade of signals that provoke dormant cancer cells to start multiplying rapidly.

But EGFR signaling is only the initial spark that ignites the fire. “We found that once dormant cancer cells are awakened, they retain what we call an ‘awakening memory,’” Zhang says. “They no longer require ongoing inflammatory signals from the microenvironment to stay active [growing and multiplying]—they remember the awakened state.”

While signals related to inflammation are necessary to awaken dormant cancer cells, exactly how much signaling is needed remains unclear. “This aspect of cancer biology is particularly challenging because multiple signals contribute to the state change in these dormant cells,” Zhang says.

The team has already identified one key player in the awakening process but understanding the full set of signals and how each contributes is far more complex—a question they are continuing to investigate in their new work.

Studying these pivotal changes in the lives of cancer cells—such as their transition from dormancy to active growth—will deepen our scientific understanding of metastasis and, as researchers in the Weinberg Lab hope, lead to more effective treatments for patients with metastatic cancers.

Locally produced proteins help mitochondria function

One of the ways that cells ensure proteins end up where they're needed is creating them at that location, through a process called localized translation. New research from the Weissman Lab has expanded our understanding localized translation at mitochondria and sheds light on the organizational principles of genes and the proteins they encode.

Greta Friar | Whitehead Institute
August 27, 2025

Now, Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator, and postdoc in his lab Jingchuan Luo have expanded our knowledge of localized translation at mitochondria, structures that generate energy for the cell. In a paper published in Cell on August 27, they share a new tool, LOCL-TL, for studying localized translation in close detail, and describe the discoveries it enabled about two classes of proteins that are locally translated at mitochondria.

The importance of localized translation at mitochondria relates to their unusual origin. Mitochondria were once bacteria that lived within our ancestors’ cells. Over time the bacteria lost their autonomy and became part of the larger cells, which included migrating most of their genes into the larger cell’s genome in the nucleus. Cells evolved processes to ensure that proteins needed by mitochondria that are encoded in genes in the larger cell’s genome get transported to the mitochondria. Mitochondria retain a few genes in their own genome, so production of proteins from the mitochondrial genome and that of the larger cell’s genome must be coordinated to avoid mismatched production of mitochondrial parts. Localized translation may help cells to manage the interplay between mitochondrial and nuclear protein production—among other purposes.

How to detect local protein production

For a protein to be made, genetic code stored in DNA is read into RNA, and then the RNA is read or translated by a ribosome, a cellular machine that builds a protein according to the RNA code. Weissman’s lab previously developed a method to study localized translation by tagging ribosomes near a structure of interest, and then capturing the tagged ribosomes in action and observing the proteins they are making. This approach, called proximity-specific ribosome profiling, allows researchers to see what proteins are being made where in the cell. The challenge that Luo faced was how to tweak this method to capture only ribosomes at work near mitochondria.

Ribosomes work quickly, so a ribosome that gets tagged while making a protein at the mitochondria can move on to making other proteins elsewhere in the cell in a matter of minutes. The only way researchers can guarantee that the ribosomes they capture are still working on proteins made near the mitochondria is if the experiment happens very quickly.

Weissman and colleagues had previously solved this time sensitivity problem in yeast cells with a ribosome-tagging tool called BirA that is activated by the presence of the molecule biotin. BirA is fused to the cellular structure of interest, and tags ribosomes it can touch—but only once activated. Researchers keep the cell depleted of biotin until they are ready to capture the ribosomes, to limit the time when tagging occurs. However, this approach does not work with mitochondria in mammalian cells because they need biotin to function normally, so it cannot be depleted.

Luo and Weissman adapted the existing tool to respond to blue light instead of biotin. The new tool, LOV-BirA, is fused to the mitochondria’s outer membrane. Cells are kept in the dark until the researchers are ready. Then they expose the cells to blue light, activating LOV-BirA to tag ribosomes. They give it a few minutes and then quickly extract the ribosomes. This approach proved very accurate at capturing only ribosomes working at mitochondria.

The researchers then used a method originally developed by the Weissman lab to extract the sections of RNA inside of the ribosomes. This allows them to see exactly how far along in the process of making a protein the ribosome is when captured, which can reveal whether the entire protein is made at the mitochondria, or whether it is partly produced elsewhere and only gets completed at the mitochondria.

“One advantage of our tool is the granularity it provides,” Luo says. “Being able to see what section of the protein is locally translated helps us understand more about how localized translation is regulated, which can then allow us to understand its dysregulation in disease and to control localized translation in future studies.”

Two protein groups are made at mitochondria

Using these approaches, the researchers found that about twenty percent of the genes needed in mitochondria that are located in the main cellular genome are locally translated at mitochondria. These proteins can be divided into two distinct groups with different evolutionary histories and mechanisms for localized translation.

One group consists of relatively long proteins, each containing more than 400 amino acids or protein building blocks. These proteins tend to be of bacterial origin—present in the ancestor of mitochondria—and they are locally translated in both mammalian and yeast cells, suggesting that their localized translation has been maintained through a long evolutionary history.

Like many mitochondrial proteins encoded in the nucleus, these proteins contain a mitochondrial targeting sequence (MTS), a zip code that tells the cell where to bring them. The researchers discovered that most proteins containing an MTS also contain a nearby inhibitory sequence that prevents transportation until they are done being made. This group of locally translated proteins lacks the inhibitory sequence, so they are brought to the mitochondria during their production.

Production of these longer proteins begins anywhere in the cell, and then after approximately the first 250 amino acids are made, they get transported to the mitochondria. While the rest of the protein gets made, it is simultaneously fed into a channel that brings it inside the mitochondria. This ties up the channel for a long time, limiting import of other proteins, so cells can only afford to do this simultaneous production and import for select proteins. The researchers hypothesize that these bacterial-origin proteins are given priority as an ancient mechanism to ensure that they are accurately produced and placed within mitochondria.

The second locally translated group consists of short proteins, each less than 200 amino acids long. These proteins are more recently evolved, and correspondingly, the researchers found that the mechanism for their localized translation is not shared by yeast. Their mitochondrial recruitment happens at the RNA level. Two sequences within regulatory sections of each RNA molecule that do not encode the final protein instead code for the cell’s machinery to recruit the RNAs to the mitochondria.

The researchers searched for molecules that might be involved in this recruitment, and identified the RNA binding protein AKAP1, which exists at mitochondria. When they eliminated AKAP1, the short proteins were translated indiscriminately around the cell. This provided an opportunity to learn more about the effects of localized translation, by seeing what happens in its absence. When the short proteins were not locally translated, this led to the loss of various mitochondrial proteins, including those involved in oxidative phosphorylation, our cells’ main energy generation pathway.

In future research, Weissman and Luo will delve deeper into how localized translation affects mitochondrial function and dysfunction in disease. The researchers also intend to use LOCL-TL to study localized translation in other cellular processes, including in relation to embryonic development, neural plasticity, and disease.

“This approach should be broadly applicable to different cellular structures and cell types, providing many opportunities to understand how localized translation contributes to biological processes,” Weissman says. “We’re particularly interested in what we can learn about the roles it may play in diseases including neurodegeneration, cardiovascular diseases, and cancers.”

Luo et al. “Proximity-specific ribosome profiling reveals the logic of localized mitochondrial translation.” Cell, August 27, 2025. https://doi.org/10.1016/j.cell.2025.08.002

Mapping cells in time and space: a new tool reveals a detailed history of tumor growth

Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue.

Greta Friar | Whitehead Institute
July 24, 2025

All life is connected in a vast family tree. Every organism exists in relationship to its ancestors, descendants, and cousins, and the path between any two individuals can be traced. The same is true of cells within organisms—each of the trillions of cells in the human body is produced through successive divisions from a fertilized egg, and can all be related to one another through a cellular family tree. In simpler organisms such as the worm C. elegans, this cellular family tree has been fully mapped, but the cellular family tree of a human is many times larger and more complex.

In the past, Whitehead Institute Member Jonathan Weissman and other researchers have developed lineage tracing methods to track and reconstruct the family trees of cell divisions in model organisms in order to understand more about the relationships between cells and how they assemble into tissues, organs, and—in some cases—tumors. These methods could help to answer many questions about how organisms develop and diseases like cancer are initiated and progress.

Now, Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue. The researchers used their tool, PEtracer, to observe the growth of metastatic tumors in mice. Combining lineage tracing and spatial data provided the researchers with a detailed view of how elements intrinsic to the cancer cells and from their environments influenced tumor growth, as Weissman and postdocs in his lab Luke Koblan, Kathryn Yost, and Pu Zheng, and graduate student William Colgan share in a paper published in the journal Science on July 24.

“Developing this tool required combining diverse skillsets through the sort of ambitious interdisciplinary collaboration that’s only possible at a place like Whitehead Institute,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “Luke came in with an expertise in genetic engineering, Pu in imaging, Katie in cancer biology, and William in computation but the real key to their success was their ability to work together to build PEtracer.”

“Understanding how cells move in time and space is an important way to look at biology, and here we were able to see both of those things in high resolution. The idea is that by understanding both a cell’s past and where it ends up, you can see how different factors throughout its life influenced its behaviors. In this study we use these approaches to look at tumor growth, though in principle we can now begin to apply these tools to study other biology of interest like embryonic development,” Koblan says.

Designing a tool to track cells in space and time

PEtracer tracks cells’ lineages by repeatedly adding short, predetermined codes to the DNA of cells over time. Each piece of code, called a lineage tracing mark, is made up of 5 bases, the building blocks of DNA. These marks are inserted using a gene editing technology called prime editing, which directly rewrites stretches of DNA with minimal undesired byproducts. Over time, each cell acquires more lineage tracing marks, while also maintaining the marks of its ancestors. The researchers can then compare cells’ combinations of marks to figure out relationships and reconstruct the family tree.

“We used computational modeling to design the tool from first principles, to make sure that it was highly accurate, and compatible with imaging technology. We ran many simulations to land on the optimal parameters for a new lineage tracing tool, and then engineered our system to fit those parameters,” Colgan says.

When the tissue—in this case, a tumor growing in the lung of a mouse—had sufficiently grown, the researchers collected these tissues and used advanced imaging approaches to look at each cell’s lineage relationship to other cells via the lineage tracing marks, along with its spatial position within the imaged tissue and its identity (as determined by the levels of different RNAs expressed in each cell). PEtracer is compatible with both imaging approaches and sequencing methods that capture genetic information from single cells.

“Making it possible to collect and analyze all of this data from the imaging was a large challenge,” Zheng says. “What’s particularly exciting to me is not just that we were able to collect terabytes of data, but that we designed the project to collect data that we knew we could use to answer important questions and drive biological discovery.”

Reconstructing the history of a tumor

Combining the lineage tracing, gene expression, and spatial data let the researchers understand how the tumor grew. They could tell how closely related neighboring cells are and compare their traits. Using this approach, the researchers found that the tumors they were analyzing were made up of four distinct modules, or neighborhoods, of cells.

The tumor cells closest to the lung, the most nutrient-dense region, were the most fit, meaning their lineage history indicated the highest rate of cell division over time. Fitness in cancer cells tends to correlate to how aggressively tumors will grow.

The cells at the “leading edge” of the tumor, the far side from the lung, were more diverse and not as fit. Below the leading edge was a low-oxygen neighborhood of cells that might once have been leading edge cells, now trapped in a less desirable spot. Between these cells and the lung-adjacent cells was the tumor core, a region with both living and dead cells as well as cellular debris.

The researchers found that cancer cells across the family tree were equally likely to end up in most of the regions, with the exception of the lung adjacent region, where a few branches of the family tree dominated. This suggests that the cancer cells’ differing traits were heavily influenced by their environments, or the conditions in their local neighborhoods, rather than their family history. Further evidence of this point was that expression of certain fitness-related genes, such as Fgf1/Fgfbp1, correlated to a cell’s location rather than its ancestry. However, lung adjacent cells also had inherited traits that gave them an edge, including expression of the fitness-related gene Cldn4­—showing that family history influenced outcomes as well.

These findings demonstrate how cancer growth is influenced both by factors intrinsic to certain lineages of cancer cells and by environmental factors that shape the behavior of cancer cells exposed to them.

“By looking at so many dimensions of the tumor in concert, we could gain insights that would not have been possible with a more limited view,” Yost says. “Being able to characterize different populations of cells within a tumor will enable researchers to develop therapies that target the most aggressive populations more effectively.”

“Now that we’ve done the hard work of designing the tool, we’re excited to apply it to look at all sorts of questions in health and disease, in embryonic development, and across other model species, with an eye toward understanding important problems in human health,” Koblan says. “The data we collect will also be useful for training AI models of cellular behavior. We’re excited to share this technology with other researchers and see what we all can discover.”

Luke W. Koblan, Kathryn E. Yost, Pu Zheng, William N. Colgan, Matthew G. Jones, Dian Yang, Arhan Kumar, Jaspreet Sandhu, Alexandra Schnell, Dawei Sun, Can Ergen, Reuben A. Saunders, Xiaowei Zhuang, William E. Allen, Nir Yosef, Jonathan S. Weissman. “High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer.” Science, online July 24, 2025. https://doi.org/10.1126/science.adx3800

Putting liver cells in context: new method combines imaging and sequencing to study gene function in living tissue

Researchers in the Weissman Lab have developed a powerful approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue, with the goal of discovering how genes control organ function and disease.

Whitehead Institute
June 12, 2025

 

However, capturing both the “visuals and sound” of biological data, such as gene expression and cell structure data, from the same cells requires researchers to develop new approaches. They also have to make sure that the data they capture accurately reflects what happens in living organisms, including how cells interact with each other and their environments.

Whitehead Institute and Harvard University researchers have taken on these challenges and developed Perturb-Multimodal (Perturb-Multi), a powerful new approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue. The method, described in Cell on June 12, aims to accelerate discovery of how genes control organ function and disease.

The research team, led by Whitehead Institute Member Jonathan Weissman and then-graduate student in his lab Reuben Saunders, along with Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University, and then-postdoc in her lab Will Allen, created a system that can test hundreds of different genetic modifications within a single mouse liver while capturing multiple types of data from the same cells.

“Understanding how our organs work requires looking at many different aspects of cell biology at once,” Saunders says. “With Perturb-Multi, we can see how turning off specific genes changes not just what other genes are active, but also how proteins are distributed within cells, how cellular structures are organized, and where cells are located in the tissue. It’s like having multiple specialized microscopes all focused on the same experiment.”

“This approach accelerates discovery by both allowing us to test the functions of many different genes at once, and then for each gene, allowing us to measure many different functional outputs or cell properties at once—and we do that in intact tissue from animals,” says Zhuang, who is also an HHMI Investigator.

A more efficient approach to genetic studies

Traditional genetic studies in mice often turn off one gene in an animal, and then observe what changes in that gene’s absence to learn about what the gene does. The researchers designed their approach to turn off hundreds of different genes across a single liver, while still only turning off one gene per cell—using what is known as a mosaic approach. This allowed them to study the roles of hundreds of individual genes at once in a single animal. The researchers then collected diverse types of data from cells across the same liver to get a full picture of the consequences of turning off the genes.

“Each cell serves as its own experiment, and because all the cells are in the same animal, we eliminate the variability that comes from comparing different mice,” Saunders says. “Every cell experiences the same physiological conditions, diet, and environment, making our comparisons much more precise.”

“The challenge we faced was that tissues, to perform their functions, rely on thousands of genes, expressed in many different cells, working together. Each gene, in turn, can control many aspects of a cell’s function. Testing these hundreds of genes in mice using current methods would be extremely slow and expensive—near impossible in practice,” Allen says.

Revealing new biology through combined measurements

The team applied Perturb-Multi to study genetic controls of liver physiology and function. Their study led to discoveries in three important aspects of liver biology: fat accumulation in liver cells—a precursor to liver disease; stress responses; and hepatocyte zonation (how liver cells specialize, assuming different traits and functions, based on their location within the liver).

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

One striking finding emerged from studying genes that, when disrupted, cause fat accumulation in liver cells. The imaging data revealed that four different genes all led to similar fat droplet accumulation, but the sequencing data showed they did so through three completely different mechanisms.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says. “The imaging told us which genes affect fat accumulation, while the sequencing revealed whether this was due to increased fat production, cellular stress, or other pathways. This kind of mechanistic insight could be crucial for developing targeted therapies for fatty liver disease.”

The researchers also discovered new regulators of liver cell zonation. Unexpectedly, the newly discovered regulators include genes involved in modifying the extracellular matrix—the scaffolding between cells. “We found that cells can change their specialized functions without physically moving to a different zone,” Saunders says. “This suggests that liver cell identity is more flexible than previously thought.”

Technical innovation enables new science

Developing Perturb-Multi required solving several technical challenges. The team created new methods for preserving the content of interest in cells—RNA and proteins—during tissue processing, for collecting many types of imaging data and single-cell gene expression data from tissue samples that have been fixed with a preservative, and for integrating multiple types of data from the same cells.

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

The two components of Perturb-Multi—the imaging and sequencing assays—together, applied to the same tissue, provide insights that are unattainable through either assay alone.

“Each component had to work perfectly while not interfering with the others,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “The technical development took considerable effort, but the payoff is a system that can reveal biology we simply couldn’t see before.”

Expanding to new organs and other contexts

The researchers plan to expand Perturb-Multi to other organs, including the brain, and to study how genetic changes affect organ function under different conditions like disease states or dietary changes.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says.

“We’re also excited about using the data we generate to train machine learning models,” adds Saunders. “With enough examples of how genetic changes affect cells, we could eventually predict the effects of mutations without having to test them experimentally—a ‘virtual cell’ that could accelerate both research and drug development.”

“Perturbation data are critical for training such AI models and the paucity of existing perturbation data represents a major hindrance in such ‘virtual cell’ efforts,” Zhuang says. “We hope Perturb-Multi will fill this gap by accelerating the collection of perturbation data.”

The approach is designed to be scalable, with the potential for genome-wide studies that test thousands of genes simultaneously. As sequencing and imaging technologies continue to improve, the researchers anticipate that Perturb-Multi will become even more powerful and accessible to the broader research community.

“Our goal is to keep scaling up. We plan to do genome-wide perturbations, study different physiological conditions, and look at different organs,” says Weissman. “That we can now collect so many types of data from so many cells, at speed, is going to be critical for building AI models like virtual cells, and I think it’s going to help us answer previously unsolvable questions about health and disease.”

Notes

Reuben A. Saunders, William E. Allen, Xingjie Pan, Jaspreet Sandhu, Jiaqi Lu, Thomas K. Lau, Karina Smolyar, Zuri A. Sullivan, Catherine Dulac, Jonathan S. Weissman, Xiaowei Zhuang. “Perturb-Multimodal: a Platform for Pooled Genetic Screens with Sequencing and Imaging in Intact Mammalian Tissue.” Cell, June 12, 2025. DOI: 10.1016/j.cell.2025.05.022.

A selfish gene unlike any other

Certain genes are “selfish," cheating the rules of inheritance to increase their chances of being transmitted. Researchers in the Yamashita Lab have uncovered a unique "self-limiting" mechanism keeping the selfish gene Stellate in check

Shafaq Zia | Whitehead Institute
May 7, 2025

When a species reproduces, typically, each parent passes on one of their two versions, or alleles, of a given gene to their offspring. But not all alleles play fair in their quest to be passed onto future generations.

Certain alleles, called meiotic drivers, are “selfish”—they cheat the rules of inheritance to increase their chances of being transmitted, often at the expense of the organism’s fitness.

The lab of Whitehead Institute Member Yukiko Yamashita investigates how genetic information is transmitted across generations through the germline—cells that give rise to egg and sperm. Now, Yamashita and first author Xuefeng Meng, a graduate student in the Yamashita Lab, have discovered a meiotic driver that operates differently from previously known drivers.

The researchers’ findings, published online in Science Advances on May 7, reveal that the Stellate (Ste) gene—which has multiple copies located close to one another—on the X chromosome in Drosophila melanogaster, a fruit fly species, is a meiotic driver that biases the transmission of the X chromosome. However, it also has a unique “self-limiting” mechanism that helps preserve the organism’s ability to have male offspring.

“This mechanism is an inherent remedy to the gene’s selfish drive,” says Yamashita, who is also a professor of biology at Massachusetts Institute of Technology and an investigator of the Howard Hughes Medical Institute. “Without it, the gene could severely skew the sex ratio in a population and drive the species to extinction—a paradox that has been recognized for a long time.”

Fatal success

Meiosis is a key process underlying sexual reproduction. This is when cells from the germline undergo two rounds of specialized cell division—meiosis I and meiosis II—to form gametes (egg and sperm cells). In males, this typically results in an equal number of X-bearing and Y-bearing sperm, which ensures an equal chance of having a male or female offspring.

Meiotic drivers located on sex chromosomes can skew this sex ratio by selectively destroying gametes that do not carry the driver allele. Among them is the meiotic driver Ste.

In male germline cells of fruit flies, Ste is kept in check by small RNA molecules, called piRNAs, produced by Suppressor of Stellate (Su(Ste)) located on the Y chromosome. These RNA molecules recruit special proteins to silence Ste RNA. This prevents the production of Ste protein that would otherwise disrupt the development of Y-bearing sperm, which helps maintain the organism’s ability to have male offspring.

“But the suppressing mechanism isn’t foolproof,” Meng explains. “When the meiotic driver and its suppressor are located on different chromosomes, they can get separated during reproduction, leaving the driver unchecked in the next generation.”

A skewed sex ratio toward females offers a short-term advantage: having more females than males could increase a population’s reproductive potential. But in the long run, the meiotic driver risks fatal success—driving the species toward extinction through depletion of males.

Interestingly, prior research suggests that un-silencing Ste only modestly skews a population’s sex ratio, even in the absence of the suppressor, unlike other meiotic drivers that almost exclusively produce females in the progeny. Could another mechanism be at play, keeping Ste’s selfish drive in check?

Practicing self-restraint

To explore this intriguing possibility, researchers in the Yamashita Lab began by examining the process of sperm development. Under moderate Ste expression, pre-meiotic germ cell development and meiosis proceeded normally but defects in sperm development began to emerge soon after. Specifically, a subset of spermatids—immature sperm cells produced after meiosis—failed to incorporate essential DNA-packaging proteins called protamines, which are required to preserve the integrity of genetic information in sperm.

To confirm if the spermatids impacted were predominantly those that carried the Y chromosome, the researchers used an imaging technique called immunofluorescence staining, which uses antibodies to attach fluorescent molecules to a protein of interest, making it glow. They combined this with a technique called FISH (fluorescence in-situ hybridization), which tags the X and Y chromosomes with fluorescent markers, allowing researchers to distinguish between cells that will become X-bearing or Y-bearing following meiosis.

Indeed, the team found that while Ste protein is present in all spermatocytes before meiosis I, it unevenly divides between the two daughter cells—a phenomenon called asymmetric segregation—during meiosis I and gets concentrated in Y-bearing spermatids, eventually inducing DNA-packaging defects in these spermatids.

These findings clarified Ste’s role as a meiotic driver but the researchers still wondered why expression of Ste only led to a moderate sex ratio distortion. The answer soon became clear when they observed Ste undergo another round of asymmetric segregation during meiosis II. This meant that even if a secondary spermatocyte inherited Ste protein after meiosis I, only half of the spermatids produced in this round of cell division ended up retaining the protein. Hence, only half of the Y-bearing spermatids were going to be killed off.

“This self-limiting mechanism is the ultimate solution to the driver-suppressor separation problem,” says Yamashita. “But the idea is so unconventional that had it been proposed as just a theory, without the evidence we have now, it would’ve been completely dismissed.”

These findings have solved some questions and raised others: Unlike female meiosis, which is known to be asymmetrical, male meiosis has traditionally been considered symmetrical. Does the unequal segregation of Ste suggest there’s an unknown asymmetry in male meiosis? Do meiotic drivers like Ste trigger this asymmetry, or do they simply exploit it to limit their selfish drive?

Answering them is the next big step for Yamashita and her colleagues. “This could fundamentally change our understanding of male meiosis,” she says. “The best moments in science are when textbook knowledge is challenged and it turns out to have been tunnel vision.”

Manipulating time with torpor

New research from the Hrvatin Lab recently published in Nature Aging indicates that inducing a hibernation-like state in mice slows down epigenetic changes that accompany aging.

Shafaq Zia | Whitehead Institute
March 7, 2025

Surviving extreme conditions in nature is no easy feat. Many species of mammals rely on special adaptations called daily torpor and hibernation to endure periods of scarcity. These states of dormancy are marked by a significant drop in body temperature, low metabolic activity, and reduced food intake—all of which help the animal conserve energy until conditions become favorable again.

The lab of Whitehead Institute Member Siniša Hrvatin studies daily torpor, which lasts several hours, and its longer counterpart, hibernation, in order to understand their effects on tissue damage, disease progression, and aging. In their latest study, published in Nature Aging on March 7, first author Lorna Jayne, Hrvatin, and colleagues show that inducing a prolonged torpor-like state in mice slows down epigenetic changes that accompany aging.

“Aging is a complex phenomenon that we’re just starting to unravel,” says Hrvatin, who is also an assistant professor of biology at Massachusetts Institute of Technology. “Although the full relationship between torpor and aging remains unclear, our findings point to decreased body temperature as the central driver of this anti-aging effect.”

Tampering with the biological clock

Aging is a universal process, but scientists have long struggled to find a reliable metric for measuring it. Traditional clocks fall short because biological age doesn’t always align with chronology—cells and tissues in different organisms age at varying rates.

To solve this dilemma, scientists have turned to studying molecular processes that are common to aging across many species. This, in the past decade, has led to the development of epigenetic clocks, new computational tools that can estimate an organism’s age by analyzing the accumulation of epigenetic marks in cells over time.

Think of epigenetic marks as tiny chemical tags that cling either to the DNA itself or to the proteins, called histones, around which the DNA is wrapped. Histones act like spools, allowing long strands of DNA to coil around them, much like thread around a bobbin. When epigenetic tags are added to histones, they can compact the DNA, preventing genetic information from being read, or loosen it, making the information more accessible. When epigenetic tags attach directly to DNA, they can alter how the proteins that “read” a gene bind to the DNA.

While it’s unclear if epigenetic marks are a cause or consequence of aging, this much is evident: these marks change over an organism’s lifespan, altering how genes are turned on or off, without modifying the underlying DNA sequence. These changes have enabled researchers to track the biological age of individual cells and tissues using dedicated epigenetic clocks.

In nature, states of stasis like hibernation and daily torpor help animals survive by conserving energy and avoiding predators. But now, emerging research in marmots and bats hints that hibernation may also slow down epigenetic aging, prompting researchers to explore whether there’s a deeper connection between prolonged bouts of torpor and longevity.

However, investigating this link has been challenging, as the mechanisms that trigger, regulate, and sustain torpor remain largely unknown. In 2020, Hrvatin and colleagues made a breakthrough by identifying neurons in a specific region of the mouse hypothalamus, known as the avMLPA, which act as core regulators of torpor.

“This is when we realized that we could leverage this system to induce torpor and explore mechanistically how the state of torpor might have beneficial effects on aging,” says Jayne. “You can imagine how difficult it is to study this in natural hibernators because of accessibility and the lack of tools to manipulate them in sophisticated ways.”

The age-old mystery

The researchers began by injecting adeno-associated virus in mice, a gene delivery vehicle that enables scientists to introduce new genetic material into target cells. They employed this technology to instruct neurons in the mice’s avMLPA region to produce a special receptor called Gq-DREADD, which does not respond to the brain’s natural signals but can be chemically activated by a drug. When the researchers administered this drug to the mice, it bound to the Gq-DREADD receptors, activating the torpor-regulating neurons and triggering a drop in the animals’ body temperature.

However, to investigate the effects of torpor on longevity, the researchers needed to maintain these mice in a torpor-like state for days to weeks. To achieve this, the mice were continuously administered the drug through drinking water.

The mice were kept in a torpor-like state with periodic bouts of arousal for a total of nine months. The researchers measured the blood epigenetic age of these mice at the 3-, 6-, and 9-month marks using the mammalian blood epigenetic clock. By the 9-month mark, the torpor-like state had reduced blood epigenetic aging in these mice by approximately 37%, making them biologically three months younger than their control counterparts.

To further assess the effects of torpor on aging,  the group evaluated these mice using the mouse clinical frailty index, which includes measurements like tail stiffening, gait, and spinal deformity that are commonly associated with aging. As expected, mice in the torpor-like state had a lower frailty index compared to the controls.

With the anti-aging effects of the torpor-like state established, the researchers sought to understand how each of the key factors underlying torpor—decreased body temperature, low metabolic activity, and reduced food intake—contributed to longevity.

To isolate the effects of reduced metabolic rate, the researchers induced a torpor-like state in mice, while maintaining the animal’s normal body temperature. After three months, the blood epigenetic age of these mice was similar to that of the control group, suggesting that low metabolic rate alone does not slow down epigenetic aging.

Next, Hrvatin and colleagues isolated the impact of low caloric intake on blood epigenetic aging by restricting the food intake of mice in the torpor-like state, while maintaining their normal body temperature. After three months, these mice were a similar blood epigenetic age as the control group.

When both low metabolic rate and reduced food intake were combined, the mice still exhibited higher blood epigenetic aging after three months compared to mice in the torpor state with low body temperature. These findings, combined, led the researchers to conclude that neither low metabolic rate nor reduced caloric intake alone are sufficient to slow down blood epigenetic aging. Instead, a drop in body temperature is necessary for the anti-aging effects of torpor.

Although the exact mechanisms linking low body temperature and epigenetic aging are unclear, the team hypothesizes that it may involve the cell cycle, which regulates how cells grow and divide: lower body temperatures can potentially slow down cellular processes, including DNA replication and mitosis. This, over time, may impact cell turnover and aging. With further research, the Hrvatin Lab aims to explore this link in greater depth and shed light on the lingering mystery.

Kingdoms collide as bacteria and cells form captivating connections

Studying the pathogen R. parkeri, researchers discovered the first evidence of extensive and stable interkingdom contacts between a pathogen and a eukaryotic organelle.

Lillian Eden | Department of Biology
January 24, 2025

In biology textbooks, the endoplasmic reticulum is often portrayed as a distinct, compact organelle near the nucleus, and is commonly known to be responsible for protein trafficking and secretion. In reality, the ER is vast and dynamic, spread throughout the cell and able to establish contact and communication with and between other organelles. These membrane contacts regulate processes as diverse as fat metabolism, sugar metabolism, and immune responses.

Exploring how pathogens manipulate and hijack essential processes to promote their own life cycles can reveal much about fundamental cellular functions and provide insight into viable treatment options for understudied pathogens.

New research from the Lamason Lab in the Department of Biology at MIT recently published in the Journal of Cell Biology has shown that Rickettsia parkeri, a bacterial pathogen that lives freely in the cytosol, can interact in an extensive and stable way with the rough endoplasmic reticulum, forming previously unseen contacts with the organelle.

It’s the first known example of a direct interkingdom contact site between an intracellular bacterial pathogen and a eukaryotic membrane.

The Lamason Lab studies R. parkeri as a model for infection of the more virulent Rickettsia rickettsii. R. rickettsii, carried and transmitted by ticks, causes Rocky Mountain Spotted Fever. Left untreated, the infection can cause symptoms as severe as organ failure and death.

Rickettsia is difficult to study because it is an obligate pathogen, meaning it can only live and reproduce inside living cells, much like a virus. Researchers must get creative to parse out fundamental questions and molecular players in the R. parkeri life cycle, and much remains unclear about how R. parkeri spreads.

Detour to the junction

First author Yamilex Acevedo-Sánchez, a BSG-MSRP-Bio program alum and a graduate student at the time, stumbled across the ER and R. parkeri interactions while trying to observe Rickettsia reaching a cell junction.

The current model for Rickettsia infection involves R. parkeri spreading cell to cell by traveling to the specialized contact sites between cells and being engulfed by the neighboring cell in order to spread. Listeria monocytogenes, which the Lamason Lab also studies, uses actin tails to forcefully propel itself into a neighboring cell. By contrast, R. parkeri can form an actin tail, but loses it before reaching the cell junction. Somehow, R. parkeri is still able to spread to neighboring cells.

After an MIT seminar about the ER’s lesser-known functions, Acevedo-Sánchez developed a cell line to observe whether Rickettsia might be spreading to neighboring cells by hitching a ride on the ER to reach the cell junction.

Instead, she saw an unexpectedly high percentage of R. parkeri surrounded and enveloped by the ER, at a distance of about 55 nanometers. This distance is significant because membrane contacts for interorganelle communication in eukaryotic cells form connections from 10-80 nanometers wide. The researchers ruled out that what they saw was not an immune response, and the sections of the ER interacting with the R. parkeri were still connected to the wider network of the ER.

“I’m of the mind that if you want to learn new biology, just look at cells,” Acevedo-Sánchez says. “Manipulating the organelle that establishes contact with other organelles could be a great way for a pathogen to gain control during infection.”

The stable connections were unexpected because the ER is constantly breaking and reforming connections, lasting seconds or minutes. It was surprising to see the ER stably associating around the bacteria. As a cytosolic pathogen that exists freely in the cytosol of the cells it infects, it was also unexpected to see R. parkeri surrounded by a membrane at all.

Small margins

Acevedo-Sánchez collaborated with the Center for Nanoscale Systems at Harvard University to view her initial observations at higher resolution using focused ion beam scanning electron microscopy. FIB-SEM involves taking a sample of cells and blasting them with a focused ion beam in order to shave off a section of the block of cells. With each layer, a high-resolution image is taken. The result of this process is a stack of images.

From there, Acevedo-Sánchez marked what different areas of the images were — such as the mitochondria, Rickettsia, or the ER — and a program called ORS Dragonfly, a machine learning program, sorted through the thousand or so images to identify those categories. That information was then used to create 3D models of the samples.

Acevedo-Sánchez noted that less than 5 percent of R. parkeri formed connections with the ER — but small quantities of certain characteristics are known to be critical for R. parkeri infection. R. parkeri can exist in two states: motile, with an actin tail, and nonmotile, without it. In mutants unable to form actin tails, R. parkeri are unable to progress to adjacent cells — but in nonmutants, the percentage of R. parkeri that have tails starts at about 2 percent in early infection and never exceeds 15 percent at the height of it.

The ER only interacts with nonmotile R. parkeri, and those interactions increased 25-fold in mutants that couldn’t form tails.

Creating connections

Co-authors Acevedo-Sánchez, Patrick Woida, and Caroline Anderson also investigated possible ways the connections with the ER are mediated. VAP proteins, which mediate ER interactions with other organelles, are known to be co-opted by other pathogens during infection.

During infection by R. parkeri, VAP proteins were recruited to the bacteria; when VAP proteins were knocked out, the frequency of interactions between R. parkeri and the ER decreased, indicating R. parkeri may be taking advantage of these cellular mechanisms for its own purposes during infection.

Although Acevedo-Sánchez now works as a senior scientist at AbbVie, the Lamason Lab is continuing the work of exploring the molecular players that may be involved, how these interactions are mediated, and whether the contacts affect the host or bacteria’s life cycle.

Senior author and associate professor of biology Rebecca Lamason noted that these potential interactions are particularly interesting because bacteria and mitochondria are thought to have evolved from a common ancestor. The Lamason Lab has been exploring whether R. parkeri could form the same membrane contacts that mitochondria do, although they haven’t proven that yet. So far, R. parkeri is the only cytosolic pathogen that has been observed behaving this way.

“It’s not just bacteria accidentally bumping into the ER. These interactions are extremely stable. The ER is clearly extensively wrapping around the bacterium, and is still connected to the ER network,” Lamason says. “It seems like it has a purpose — what that purpose is remains a mystery.”

A new approach to modeling complex biological systems

MIT engineers’ new model could help researchers glean insights from genomic data and other huge datasets. This is potentially critical to researchers who study any kind of complex biological system, according to senior author Douglas Lauffenburger.

Anne Trafton | MIT News
November 5, 2024

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can produce enormous quantities of data from a given cellular or multicellular system.

However, making sense of this information is not always easy. This is especially true when trying to analyze complex systems such as the cascade of interactions that occur when the immune system encounters a foreign pathogen.

MIT biological engineers have now developed a new computational method for extracting useful information from these datasets. Using their new technique, they showed that they could unravel a series of interactions that determine how the immune system responds to tuberculosis vaccination and subsequent infection.

This strategy could be useful to vaccine developers and to researchers who study any kind of complex biological system, says Douglas Lauffenburger, the Ford Professor of Engineering in the departments of Biological Engineering, Biology, and Chemical Engineering.

“We’ve landed on a computational modeling framework that allows prediction of effects of perturbations in a highly complex system, including multiple scales and many different types of components,” says Lauffenburger, the senior author of the new study.

Shu Wang, a former MIT postdoc who is now an assistant professor at the University of Toronto, and Amy Myers, a research manager in the lab of University of Pittsburgh School of Medicine Professor JoAnne Flynn, are the lead authors of a new paper on the work, which appears today in the journal Cell Systems.

Modeling complex systems

When studying complex biological systems such as the immune system, scientists can extract many different types of data. Sequencing cell genomes tells them which gene variants a cell carries, while analyzing messenger RNA transcripts tells them which genes are being expressed in a given cell. Using proteomics, researchers can measure the proteins found in a cell or biological system, and cytometry allows them to quantify a myriad of cell types present.

Using computational approaches such as machine learning, scientists can use this data to train models to predict a specific output based on a given set of inputs — for example, whether a vaccine will generate a robust immune response. However, that type of modeling doesn’t reveal anything about the steps that happen in between the input and the output.

“That AI approach can be really useful for clinical medical purposes, but it’s not very useful for understanding biology, because usually you’re interested in everything that’s happening between the inputs and outputs,” Lauffenburger says. “What are the mechanisms that actually generate outputs from inputs?”

To create models that can identify the inner workings of complex biological systems, the researchers turned to a type of model known as a probabilistic graphical network. These models represent each measured variable as a node, generating maps of how each node is connected to the others.

Probabilistic graphical networks are often used for applications such as speech recognition and computer vision, but they have not been widely used in biology.

Lauffenburger’s lab has previously used this type of model to analyze intracellular signaling pathways, which required analyzing just one kind of data. To adapt this approach to analyze many datasets at once, the researchers applied a mathematical technique that can filter out any correlations between variables that are not directly affecting each other. This technique, known as graphical lasso, is an adaptation of the method often used in machine learning models to strip away results that are likely due to noise.

“With correlation-based network models generally, one of the problems that can arise is that everything seems to be influenced by everything else, so you have to figure out how to strip down to the most essential interactions,” Lauffenburger says. “Using probabilistic graphical network frameworks, one can really boil down to the things that are most likely to be direct and throw out the things that are most likely to be indirect.”

Mechanism of vaccination

To test their modeling approach, the researchers used data from studies of a tuberculosis vaccine. This vaccine, known as BCG, is an attenuated form of Mycobacterium bovis. It is used in many countries where TB is common but isn’t always effective, and its protection can weaken over time.

In hopes of developing more effective TB protection, researchers have been testing whether delivering the BCG vaccine intravenously or by inhalation might provoke a better immune response than injecting it. Those studies, performed in animals, found that the vaccine did work much better when given intravenously. In the MIT study, Lauffenburger and his colleagues attempted to discover the mechanism behind this success.

The data that the researchers examined in this study included measurements of about 200 variables, including levels of cytokines, antibodies, and different types of immune cells, from about 30 animals.

The measurements were taken before vaccination, after vaccination, and after TB infection. By analyzing the data using their new modeling approach, the MIT team was able to determine the steps needed to generate a strong immune response. They showed that the vaccine stimulates a subset of T cells, which produce a cytokine that activates a set of B cells that generate antibodies targeting the bacterium.

“Almost like a roadmap or a subway map, you could find what were really the most important paths. Even though a lot of other things in the immune system were changing one way or another, they were really off the critical path and didn’t matter so much,” Lauffenburger says.

The researchers then used the model to make predictions for how a specific disruption, such as suppressing a subset of immune cells, would affect the system. The model predicted that if B cells were nearly eliminated, there would be little impact on the vaccine response, and experiments showed that prediction was correct.

This modeling approach could be used by vaccine developers to predict the effect their vaccines may have, and to make tweaks that would improve them before testing them in humans. Lauffenburger’s lab is now using the model to study the mechanism of a malaria vaccine that has been given to children in Kenya, Ghana, and Malawi over the past few years.

“The advantage of this computational approach is that it filters out many biological targets that only indirectly influence the outcome and identifies those that directly regulate the response. Then it’s possible to predict how therapeutically altering those biological targets would change the response. This is significant because it provides the basis for future vaccine and trial designs that are more data driven,” says Kathryn Miller-Jensen, a professor of biomedical engineering at Yale University, who was not involved in the study.

Lauffenburger’s lab is also using this type of modeling to study the tumor microenvironment, which contains many types of immune cells and cancerous cells, in hopes of predicting how tumors might respond to different kinds of treatment.

The research was funded by the National Institute of Allergy and Infectious Diseases.