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Seven new faculty join the MIT School of Science

Departments of Biology and Brain and Cognitive Sciences welcome new professors.

School of Science
February 16, 2022

This winter, seven new faculty members join the MIT School of Science in the departments of Biology and Brain and Cognitive Sciences.

Siniša Hrvatin studies how animals initiate, regulate, and survive states of stasis, such as torpor and hibernation. To survive extreme environments, many animals have evolved the ability to decrease metabolic rate and body temperature and enter dormant states. His long-term goal is to harness the potential of these biological adaptations to advance medicine. Previously, he identified the neurons that regulate mouse torpor and established a platform for the development of cell-type-specific viral drivers.

Hrvatin earned his bachelor’s degree in biochemical sciences in 2007 and his PhD in stem cell and regenerative medicine in 2013, both from Harvard University. He was then a postdoc in bioengineering at MIT and a postdoc in neurobiology at Harvard Medical School. Hrvatin returns to MIT as an assistant professor of biology and a member of the Whitehead Institute for Biomedical Research.

Sara Prescott investigates how sensory inputs from within the body control mammalian physiology and behavior. Specifically, she uses mammalian airways as a model system to explore how the cells that line the surface of the body communicate with parts of the nervous system. For example, what mechanisms elicit a reflexive cough? Prescott’s research considers the critical questions of how airway insults are detected, encoded, and adapted to mammalian airways with the ultimate goal of providing new ways to treat autonomic dysfunction.

Prescott earned her bachelor’s degree in molecular biology from Princeton University in 2008 followed by her PhD in developmental biology from Stanford University in 2016. Prior to joining MIT, she was a postdoc at Harvard Medical School and Howard Hughes Medical Institute. The Department of Biology welcomes Prescott as an assistant professor.

Alison Ringel is a T-cell immunologist with a background in biochemistry, biophysics, and structural biology. She investigates how environmental factors such as aging, metabolism, and diet impact tumor progress and the immune responses that cause tumor control. By mapping the environment around a tumor on a cellular level, she seeks to gain a molecular understanding of cancer risk factors.

Ringel received a bachelor’s degree in molecular biology, biochemistry, and physics from Wesleyan University, then a PhD in molecular biophysics from John Hopkins University School of Medicine. Previously, Ringel was a postdoc in the Department of Cell Biology at Harvard Medical School. She joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute of MGH, MIT and Harvard.

Francisco J. Sánchez-Rivera PhD ’16 investigates genetic variation with a focus on cancer. He integrates genome engineering technologies, genetically-engineered mouse models (GEMMs), and single cell lineage tracing and omics approaches in order to understand the mechanics of cancer development and evolution. With state-of-the-art technologies — including a CRISPR-based genome editing system he developed as a graduate student at MIT — he hopes to make discoveries in cancer genetics that will shed light on disease progression and pave the way for better therapeutic treatments.

Sánchez-Rivera received his bachelor’s degree in microbiology from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT. He then pursued postdoctoral studies at Memorial Sloan Kettering Cancer Center supported by a HHMI Hanna Gray Fellowship. Sánchez-Rivera returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute for Integrative Cancer Research at MIT.

Nidhi Seethapathi builds predictive models to help understand human movement with a combination of theory, computational modeling, and experiments. Her research focuses on understanding the objectives that govern movement decisions, the strategies used to execute movement, and how new movements are learned. By studying movement in real-world contexts using creative approaches, Seethapathi aims to make discoveries and develop tools that could improve neuromotor rehabilitation.

Seethapathi earned her bachelor’s degree in mechanical engineering from the Veermata Jijabai Technological Institute followed by her PhD in mechanical engineering from Ohio State University. In 2018, she continued to the University of Pennsylvania where she was a postdoc. She joins MIT as an assistant professor in the Department of Brain and Cognitive Sciences with a shared appointment in the Department of Electrical Engineering and Computer Science at the MIT Schwarzman College of Computing.

Hernandez Moura Silva researches how the immune system supports tissue physiology. Silva focuses on macrophages, a type of immune cell involved in tissue homeostasis. He plans to establish new strategies to explore the effects and mechanisms of such immune-related pathways, his research ultimately leading to the development of therapeutic approaches to treat human diseases.

Silva earned a bachelor’s degree in biological sciences and a master’s degree in molecular biology from the University of Brasilia. He continued to complete a PhD in immunology at the University of São Paulo School of Medicine: Heart Institute. Most recently, he acted as the Bernard Levine Postdoctoral Fellow in immunology and immuno-metabolism at the New York University School of Medicine: Skirball Institute of Biomolecular Medicine. Silva joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute.

Yadira Soto-Feliciano PhD ’16 studies chromatin — the complex of DNA and proteins that make up chromosomes. She combines cancer biology and epigenetics to understand how certain proteins affect gene expression and, in turn, how they impact the development of cancer and other diseases. In decoding the chemical language of chromatin, Soto-Feliciano pursues a basic understanding of gene regulation that could improve the clinical management of diseases associated with their dysfunction.

Soto-Feliciano received her bachelor’s degree in chemistry from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT, where she was also a research fellow with the Koch Institute. Most recently, she was the Damon Runyon-Sohn Pediatric Cancer Postdoctoral Fellow at The Rockefeller University. Soto-Feliciano returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute.

Whitehead Institute Member Pulin Li named an Allen Distinguished Investigator
Merrill Meadow | Whitehead Institute
February 9, 2022

Whitehead Institute Member Pulin Li has been selected by The Paul G. Allen Frontiers Group to be an Allen Distinguished Investigator. The Allen Distinguished Investigator program backs creative, early-stage research projects in biology and medical research that would not otherwise be supported by traditional research funding programs. Each Allen Distinguished Investigator award provides three years of research funding.

Li, who is also an assistant professor of biology and the Eugene Bell Career Development Professor of Tissue Engineering at Massachusetts Institute of Technology, studies how circuits of genes within individual cells enable multicellular functions and phenomena such as the patterns of varied cell types that comprise a tissue. Her lab combines approaches from synthetic biology, developmental biology, biophysics, and systems biology to quantitatively understand how cells communicate to produce those phenomena. The work could lead to ways to program stem cells to form tissues for regenerative medicine.

“I am very grateful for this generous support ,” Li says. “The Frontiers Group’s commitment to early-stage investigations is welcome by scientists who are trying to open new paths to discovery.”

Li’s project seeks to advance the field of synthetic developmental biology through improving the process researchers use to create small groups of cells that develop certain functions of organs. Known as organoids, these tissues enable researchers to learn more about how organs develop and function in both healthy and diseased states; and they could be used for rapid and accurate preclinical drug testing.

“All organs in our body are ecosystems of different cell types that constantly talk to each other and regulate each other’s fates, and the challenge researchers face is creating organoids that reflect this multifaceted interaction,” Li explains. “Organoids that include a more complex and complete suite of tissues may prove to function more like real organs. In the project supported by the Allen Distinguished Investigator award, my lab seeks to improve the development of organoids by introducing a type of supportive tissue known as the stroma.”

Most organs are made of epithelial cells juxtaposed with the stroma’s connective tissue. Within the stroma, mesenchymal cells help to orchestrate tissue formation and the spatial organization of other cell types. The versatile function of mesenchymal cells critically depends on their extraordinary capability to produce an array of molecules that can stimulate other cell types.

As a result, each population of mesenchymal cells has distinct capability to support the development of other cell types, control organ shapes, respond to tissue injury, and regulate inflammation.

“Despite the important function of mesenchymal cells,” Li says, “they are mostly missing in the organoids that researchers have thus far developed. Our goal is to engineer diverse populations of human mesenchymal cells and  reconstitute their spatial relationship and communication with other cell types in the stroma.

“Ultimately, we believe, these synthetically engineered stroma will help unleash the full potential of organoids as useful tools for studying organ formation and physiology.”

The Paul G. Allen Frontiers Group was founded in 2016 by the late philanthropist Paul G. Allen to explore the landscape of bioscience and to identify and foster ideas that will change the world. Its Allen Distinguished Investigators program advances frontier explorations with exceptional creativity and potential impact.

Probing how proteins pair up inside cells

MIT biologists drilled down into how proteins recognize and bind to one another, informing drug treatments for cancer.

Raleigh McElvery | Department of Biology
February 3, 2022

Despite its minute size, a single cell contains billions of molecules that bustle around and bind to one another, carrying out vital functions. The human genome encodes about 20,000 proteins, most of which interact with partner proteins to mediate upwards of 400,000 distinct interactions. These partners don’t just latch onto one another haphazardly; they only bind to very specific companions that they must recognize inside the crowded cell. If they create the wrong pairings — or even the right pairings at the wrong place or wrong time — cancer or other diseases can ensue. Scientists are hard at work investigating these protein-protein relationships, in order to understand how they work, and potentially create drugs that disrupt or mimic them to treat disease.

The average human protein is composed of approximately 400 building blocks called amino acids, which are strung together and folded into a complex 3D structure. Within this long string of building blocks, some proteins contain stretches of four to six amino acids called short linear motifs (SLiMs), which mediate protein-protein interactions. Despite their simplicity and small size, SLiMs and their binding partners facilitate key cellular processes. However, it’s been historically difficult to devise experiments to probe how SLiMs recognize their specific binding partners.

To address this problem, a group led by Theresa Hwang PhD ’21 designed a screening method to understand how SLiMs selectively bind to certain proteins, and even distinguish between those with similar structures. Using the detailed information they gleaned from studying these interactions, the researchers created their own synthetic molecule capable of binding extremely tightly to a protein called ENAH, which is implicated in cancer metastasis. The team shared their findings in a pair of eLife studies, one published on Dec. 2, 2021, and the other published Jan. 25.

“The ability to test hundreds of thousands of potential SLiMs for binding provides a powerful tool to explore why proteins prefer specific SLiM partners over others,” says Amy Keating, professor of biology and biological engineering and the senior author on both studies. “As we gain an understanding of the tricks that a protein uses to select its partners, we can apply these in protein design to make our own binders to modulate protein function for research or therapeutic purposes.”

Most existing screens for SLiMs simply select for short, tight binders, while neglecting SLiMs that don’t grip their partner proteins quite as strongly. To survey SLiMs with a wide range of binding affinities, Keating, Hwang, and their colleagues developed their own screen called MassTitr.

The researchers also suspected that the amino acids on either side of the SLiM’s core four-to-six amino acid sequence might play an underappreciated role in binding. To test their theory, they used MassTitr to screen the human proteome in longer chunks comprised of 36 amino acids, in order to see which “extended” SLiMs would associate with the protein ENAH.

ENAH, sometimes referred to as Mena, helps cells to move. This ability to migrate is critical for healthy cells, but cancer cells can co-opt it to spread. Scientists have found that reducing the amount of ENAH decreases the cancer cell’s ability to invade other tissues — suggesting that formulating drugs to disrupt this protein and its interactions could treat cancer.

Thanks to MassTitr, the team identified 33 SLiM-containing proteins that bound to ENAH — 19 of which are potentially novel binding partners. They also discovered three distinct patterns of amino acids flanking core SLiM sequences that helped the SLiMs bind even tighter to ENAH. Of these extended SLiMs, one found in a protein called PCARE bound to ENAH with the highest known affinity of any SLiM to date.

Next, the researchers combined a computer program called dTERMen with X-ray crystallography in order understand how and why PCARE binds to ENAH over ENAH’s two nearly identical sister proteins (VASP and EVL). Hwang and her colleagues saw that the amino acids flanking PCARE’s core SliM caused ENAH to change shape slightly when the two made contact, allowing the binding sites to latch onto one another. VASP and EVL, by contrast, could not undergo this structural change, so the PCARE SliM did not bind to either of them as tightly.

Inspired by this unique interaction, Hwang designed her own protein that bound to ENAH with unprecedented affinity and specificity. “It was exciting that we were able to come up with such a specific binder,” she says. “This work lays the foundation for designing synthetic molecules with the potential to disrupt protein-protein interactions that cause disease — or to help scientists learn more about ENAH and other SLiM-binding proteins.”

Ylva Ivarsson, a professor of biochemistry at Uppsala University who was not involved with the study, says that understanding how proteins find their binding partners is a question of fundamental importance to cell function and regulation. The two eLife studies, she explains, show that extended SLiMs play an underappreciated role in determining the affinity and specificity of these binding interactions.

“The studies shed light on the idea that context matters, and provide a screening strategy for a variety of context-dependent binding interactions,” she says. “Hwang and co-authors have created valuable tools for dissecting the cellular function of proteins and their binding partners. Their approach could even inspire ENAH-specific inhibitors for therapeutic purposes.”

Hwang’s biggest takeaway from the project is that things are not always as they seem: even short, simple protein segments can play complex roles in the cell. As she puts it: “We should really appreciate SLiMs more.”

New high-throughput method greatly expands view of how mutations impact cells

Broad scientists have developed a new approach for studying the functional effects of the millions of mutations associated with cancer and other diseases

Tom Ulrich | Broad Institute
January 27, 2022

There are millions of mutations and other genetic variations in cancer. Understanding which of these mutations is an impactful tumor “driver” compared to an innocuous “passenger”, and what each of the drivers does to the cancer cell, however, has been a challenging undertaking. Many studies rely on bespoke, time-consuming, gene-specific approaches that provide one-dimensional views into a given mutation’s broader functional impacts. Alternatively, computational predictions can provide functional insights, but those findings must then be confirmed through experiments.

Now, in a report published in Nature Biotechnology, a research team at the Broad Institute of MIT and Harvard has unveiled a massive-scale, high resolution method for functionally assessing large numbers of protein-coding mutations simultaneously, one that returns rich phenotypic information and which could potentially be used to study any mutation in any gene in cancer and perhaps other diseases. Their results, gained through proof-of-concept experiments with cancer cell lines, also show that individual mutations can have a spectrum of effects not only on their impacted genes but also on molecular pathways and cell state as a whole, and add nuance to the long-accepted practice of dividing cancer mutations into so-called “drivers” and “passengers.”

“When you look at the genetic data from patients’ tumors, you see that the majority of cancer-associated mutations are actually quite rare, which means we have few insights into what these mutations do,” said Jesse Boehm of the Broad’s Cancer Program, who was co-senior author of the study with Aviv Regev, a Broad core institute member now at Genentech, a member of the Roche Group. “For cancer precision medicine to become a reality, we need a firm understanding of the function of each mutation, but a major challenge has been defining an experimental approach that could be implemented in the lab at the scale required. This new method may be the tool we need.”

The new method, called single-cell expression-based variant impact phenotyping (sc-eVIP), builds on Perturb-seq — an approach developed in 2016 by Regev and colleagues for manipulating genes and exploring the consequences of those manipulations using high-throughput single-cell RNA sequencing —  and eVIP, a method also developed in 2016 by Boehm and colleagues for profiling cancer variants at low scale using RNA measurements. While Perturb-seq assays originally relied on CRISPR to introduce mutations into cells, the sc-eVIP team adopted an overexpression-based approach, engineering DNA-barcoded gene constructs for each mutation of interest and introducing them into pools of cells in such a way that the cells expressed the mutated genes at higher-than-normal levels.

By then recording each perturbed cell’s expression profile using single cell RNA sequencing, the team could both identify which mutation a given cell carried (based on the constructs’ unique barcodes) and examine the mutation’s broader impact on the cell’s overall expression state. This approach provides a highly detailed view of a mutation’s impact on a variety of molecular pathways and circuits, and does not need to be adapted for each new gene studied.

“In a sense, we’re using the cell as a biosensor,” said Oana Ursu, a postdoctoral fellow in the Regev lab, formerly within the Broad’s Klarman Cell Observatory and now at Genentech, and co-first author of the study with JT Neal, a senior group leader in the Broad’s Cancer Program. “By looking at the expression changes that take place when we overexpress a mutated gene, we can learn whether it has a meaningful impact. But also, we can compare and categorize variants based on the changes they trigger, and look for patterns in the biology they affect.”

“Most of the technologies developed for interpreting coding variants up to now have been very scalable, but have had relatively simple readouts like cell viability or maybe looked at a single trait. Their information content has been low, and it takes a lot of work to optimize them,” said Neal. “With sc-eVIP, we’ve engineered a comprehensive approach that’s high throughput and information-rich, which could be a real boon for large-scale variant-to-function studies.”

To test sc-eVIP’s potential, the team chose to study TP53 — the most commonly mutated gene in cancer — and KRAS — which encodes a key oncogene responsible for abnormal growth of many cancers. Neal, Ursu, and their collaborators generated constructs containing 200 known TP53 and KRAS mutations (including cancer-associated mutations and control mutations known to leave gene function unaffected) and introduced them into 300,000 lung cancer cells, and captured each cell’s individual expression profile. Based on those profiles, the team categorized each mutation as either “wildtype-like” (that is, effectively functionally indistinguishable from the unmutated gene) or “putatively impactful,” from there further defining mutations based on whether they reduced or enhanced the gene’s function.

The profiles also revealed each mutation’s broader impact on cell state, based on how the activity of a variety of pathways changed across single cells. For instance, the sc-eVIP data revealed KRAS mutations that fall along a continuum in how they impact cell state at the population level, from having no impact to influencing subtle shifts in cellular abundances to causing outright activation or repression of key pathways in a majority of cells. These findings suggest that different mutations within the same gene can influence cell state along a spectrum of impact.

“The cancer community has long embraced a binary conceptual framework of ‘driver’ mutations, ones that promote cancer development and progression, versus ‘passenger’ mutations, which are completely inert and just happened to arise along the way,” Boehm noted. “These initial findings suggest that biologically those categories are likely overly simplistic, that there’s actually a continuum of functional impact from inert to completely tumorigenic.”

While the team focused on cancer-associated genes and mutations for this study, they noted that sc-eVIP is gene-agnostic, highly scalable, and that using single cell RNA sequencing as a readout offers an efficient and generalizable approach to producing rich phenotypic data. They also calculated that it should be possible to thoroughly characterize most mutations with only 20 to a few hundred cells. Based on those numbers, it may be possible with sc-eVIP to generate a first-draft functional map of more than 2 million variants in approximately 200 known cancer genes with 71 million cells.

“If we can map where every cancer-associated variant fits on the continuum of impact in a variety of cancers and cell types,” Boehm said, “we’ll have a much better grasp of how the interplay of variants affects cell state, which in turn affects cancer development, growth, and response. Such knowledge would represent a true advance toward cancer precision medicine.”

Support for this study came from the National Cancer Institute, the National Human Genome Research Institute, the Mark Foundation for Cancer Research, the Howard Hughes Medical Institute, the Broadnext10 and Variant to Function programs and the Klarman Cell Observatory at the Broad Institute, and other sources.

Paper(s) cited:

Ursu O, Neal JT, et al. Massively parallel phenotyping of coding variants in cancer with Perturb-seq. Nature Biotechnology. Online January 20, 2022. DOI:10.1038/s41587-021-01160-7.

Blending machine learning and biology to predict cell fates and other changes
Greta Friar | Whitehead Institute
February 1, 2022

Imagine a ball thrown in the air: it curves up, then down, tracing an arc to a point on the ground some distance away. The path of the ball can be described with a simple mathematical equation, and if you know the equation, you can figure out where the ball is going to land. Biological systems tend to be harder to forecast, but Whitehead Institute Member Jonathan Weissman, postdoc in his lab Xiaojie Qiu, and collaborators at the University of Pittsburgh School of Medicine are working on making the path taken by cells as predictable as the arc of a ball. Rather than looking at how cells move through space, they are considering how cells change with time.

Weissman, Qiu, and collaborators Jianhua Xing, professor of computational and systems biology at the University of Pittsburgh School of Medicine, and Xing lab graduate student Yan Zhang have built a machine learning framework that can define the mathematical equations describing a cell’s trajectory from one state to another, such as its development from a stem cell into one of several different types of mature cell. The framework, called dynamo, can also be used to figure out the underlying mechanisms—the specific cocktail of gene activity—driving changes in the cell. Researchers could potentially use these insights to manipulate cells into taking one path instead of another, a common goal in biomedical research and regenerative medicine.  

The researchers describe dynamo in a paper published in the journal Cell on February 1. They explain the framework’s many analytical capabilities and use it to help understand mechanisms of human blood cell production, such as why one type of blood cell forms first (appears more rapidly than others).

“Our goal is to move towards a more quantitative version of single cell biology,” Qiu says. “We want to be able to map how a cell changes in relation to the interplay of regulatory genes as accurately as an astronomer can chart a planet’s movement in relation to gravity, and then we want to understand and be able to control those changes.”

How to map a cell’s future journey

 Dynamo uses data from many individual cells to come up with its equations. The main information that it requires is how the expression of different genes in a cell changes from moment to moment. The researchers estimate this by looking at changes in the amount of RNA over time, because RNA is a measurable product of gene expression. In the same way that knowing the starting position and velocity of a ball is necessary to understand the arc it will follow, researchers use the starting levels of RNAs and how those RNA levels are changing to predict the path of the cell. However, calculating changes in the amount of RNA from single cell sequencing data is challenging, because sequencing only measures RNA once. Researchers must then use clues like RNA-being-made at the time of sequencing and equations for RNA turnover to estimate how RNA levels were changing. Qiu and colleagues had to improve on previous methods in several ways in order to get clean enough measurements for dynamo to work. In particular, they used a recently developed experimental method that tags new RNA to distinguish it from old RNA, and combined this with sophisticated mathematical modeling, to overcome limitations of older estimation approaches.

The researchers’ next challenge was to move from observing cells at discrete points in time to a continuous picture of how cells change. The difference is like switching from a map showing only landmarks to a map that shows the uninterrupted landscape, making it possible to trace the paths between landmarks. Led by Qiu and Zhang, the group used machine learning to reveal continuous functions that define these spaces. 

“There have been tremendous advances in methods for broadly profiling transcriptomes and other ‘omic’ information with single-cell resolution. The analytical tools for exploring these data, however, to date have been descriptive instead of predictive. With a continuous function, you can start to do things that weren’t possible with just accurately sampled cells at different states. For example, you can ask: if I changed one transcription factor, how is it going to change the expression of the other genes?” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology (MIT), a member of the Koch Institute for Integrative Biology Research at MIT, and an investigator of the Howard Hughes Medical Institute.

Dynamo can visualize these functions by turning them into math-based maps. The terrain of each map is determined by factors like the relative expression of key genes. A cell’s starting place on the map is determined by its current gene expression dynamics. Once you know where the cell starts, you can trace the path from that spot to find out where the cell will end up.

The researchers confirmed dynamo’s cell fate predictions by testing it against cloned cells–cells that share the same genetics and ancestry. One of two nearly-identical clones would be sequenced while the other clone went on to differentiate. Dynamo’s predictions for what would have happened to each sequenced cell matched what happened to its clone.

Moving from math to biological insight and non-trivial predictions

With a continuous function for a cell’s path over time determined, dynamo can then gain insights into the underlying biological mechanisms. Calculating derivatives of the function provides a wealth of information, for example by allowing researchers to determine the functional relationships between genes—whether and how they regulate each other. Calculating acceleration can show that a gene’s expression is growing or shrinking quickly even when its current level is low, and can be used to reveal which genes play key roles in determining a cell’s fate very early in the cell’s trajectory. The researchers tested their tools on blood cells, which have a large and branching differentiation tree. Together with blood cell expert Vijay Sankaran of Boston Children’s Hospital, the Dana-Farber Cancer Institute, Harvard Medical School, and Broad Institute of MIT and Harvard, and Eric Lander of Broad Institute, they found that dynamo accurately mapped blood cell differentiation and confirmed a recent finding that one type of blood cell, megakaryocytes, forms earlier than others. Dynamo also discovered the mechanism behind this early differentiation: the gene that drives megakaryocyte differentiation, FLI1, can self-activate, and because of this is present at relatively high levels early on in progenitor cells. This predisposes the progenitors to differentiate into megakaryocytes first.

The researchers hope that dynamo could not only help them understand how cells transition from one state to another, but also guide researchers in controlling this. To this end, dynamo includes tools to simulate how cells will change based on different manipulations, and a method to find the most efficient path from one cell state to another. These tools provide a powerful framework for researchers to predict how to optimally reprogram any cell type to another, a fundamental challenge in stem cell biology and regenerative medicine, as well as to generate hypotheses of how other genetic changes will alter cells’ fate. There are a variety of possible applications.

“If we devise a set of equations that can describe how genes within a cell regulate each other, we can computationally describe how to transform terminally differentiated cells into stem cells, or predict how a cancer cell may respond to various combinations of drugs that would be impractical to test experimentally,” Xing says.

Dynamo’s computational modeling can be used to predict the most likely path that a cell will follow when reprogramming one cell type to another, as well as the path that a cell will take after specific genetic manipulations. 

Dynamo moves beyond merely descriptive and statistical analyses of single cell sequencing data to derive a predictive theory of cell fate transitions. The dynamo toolset can provide deep insights into how cells change over time, hopefully making cells’ trajectories as predictable for researchers as the arc of a ball, and therefore also as easy to change as switching up a pitch.

School of Science announces 2022 Infinite Expansion Awards

Eight postdocs and research scientists within the School of Science honored for contributions to the Institute.

School of Science
January 28, 2022

The MIT School of Science has announced eight postdocs and research scientists as recipients of the 2022 Infinite Expansion Award.

The award, formerly known as the Infinite Kilometer Award, was created in 2012 to highlight extraordinary members of the MIT science community. The awardees are nominated not only for their research, but for going above and beyond in mentoring junior colleagues, participating in educational programs, and contributing to their departments, labs, and research centers, the school, and the Institute.

The 2022 School of Science Infinite Expansion winners are:

  • Héctor de Jesús-Cortés, a postdoc in the Picower Institute for Learning and Memory, nominated by professor and Department of Brain and Cognitive Sciences (BCS) head Michale Fee, professor and McGovern Institute for Brain Research Director Robert Desimone, professor and Picower Institute Director Li-Huei Tsai, professor and associate BCS head Laura Schulz, associate professor and associate BCS head Joshua McDermott, and professor and BCS Postdoc Officer Mark Bear for his “awe-inspiring commitment of time and energy to research, outreach, education, mentorship, and community;”
  • Harold Erbin, a postdoc in the Laboratory for Nuclear Science’s Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), nominated by professor and IAIFI Director Jesse Thaler, associate professor and IAIFI Deputy Director Mike Williams, and associate professor and IAIFI Early Career and Equity Committee Chair Tracy Slatyer for “provid[ing] exemplary service on the IAIFI Early Career and Equity Committee” and being “actively involved in many other IAIFI community building efforts;”
  • Megan Hill, a postdoc in the Department of Chemistry, nominated by Professor Jeremiah Johnson for being an “outstanding scientist” who has “also made exceptional contributions to our community through her mentorship activities and participation in Women in Chemistry;”
  • Kevin Kuns, a postdoc in the Kavli Institute for Astrophysics and Space Research, nominated by Associate Professor Matthew Evans for “consistently go[ing] beyond expectations;”
  • Xingcheng Lin, a postdoc in the Department of Chemistry, nominated by Associate Professor Bin Zhang for being “very talented, extremely hardworking, and genuinely enthusiastic about science;”
  • Alexandra Pike, a postdoc in the Department of Biology, nominated by Professor Stephen Bell for “not only excel[ing] in the laboratory” but also being “an exemplary citizen in the biology department, contributing to teaching, community, and to improving diversity, equity, and inclusion in the department;”
  • Nora Shipp, a postdoc with the Kavli Institute for Astrophysics and Space Research, nominated by Assistant Professor Lina Necib for being “independent, efficient, with great leadership qualities” with “impeccable” research; and
  • Jakob Voigts, a research scientist in the McGovern Institute for Brain Research, nominated by Associate Professor Mark Harnett and his laboratory for “contribut[ing] to the growth and development of the lab and its members in numerous and irreplaceable ways.”

Winners are honored with a monetary award and will be celebrated with family, friends, and nominators at a later date, along with recipients of the Infinite Mile Award.

Life Sciences at MIT is unmatched

Paul Schimmel and family’s recent donation can match up to $25 million in additional funds to support life sciences at MIT

Julia Keller | MIT School of Science
January 18, 2022

This past August, the Department of Biology and the School of Science announced the creation of the Schimmel Family Program for Life Sciences at MIT as a result of the $50 million dollar donation from Professor Emeritus Paul Schimmel PhD ’66 and his family. Half of this support is designated to be matched with donations from other supporters of the department and the life sciences research enterprise. Now, with a recent donation from Institute Professor Phillip Sharp — a friend of the Schimmels and another lifelong supporter of biology — the matching funds, and opportunities for life sciences research at MIT, continue to be unlocked.

“The life sciences educational enterprise spreads across a dozen departments at MIT,” says Schimmel of the impact of this giving. “What makes the Biology Department and the life sciences at MIT so extraordinary is the singular ability to transfer knowledge and inventions to society for its benefit.”

Schimmel and Sharp, well-matched

In August 2021, the Schimmel family committed $50 million to support the life sciences at MIT. The family’s initial gift of $25 million established the Schimmel Family Program for Life Sciences and was matched with $25 million secured from other sources in support of the Department of Biology. The remaining $25 million from the Schimmel family now serves as matching funds for future gifts supporting the life sciences. To date, $7 million in new gifts from other donors have been committed toward this effort, eliciting an additional $7 million in matching funds from the Schimmel family.

Professor Sharp is among those supporters who have joined his former colleague and friend, Paul Schimmel.

“Paul and I taught together, shared an interest in RNA biology, and have remained close friends since his move to Scripps Institute,” says Sharp of his career-long friendship with Schimmel, who is the Ernst and Jean Hahn Professor at the Skaggs Institute for Chemical Biology at the Scripps Research Institute.

Though Schimmel formally left MIT for the Scripps Institute in 1997, he remained actively involved in supporting MIT’s research enterprise, with a particular focus on MIT graduate students, through his role on the Biology Visiting Committee.

Sharp and Schimmel agree that the success of graduate students remains key to the long-term sustainability of the life sciences at MIT. “We share a passion for supporting and engaging with the next generation of outstanding biologists, scientists, and leaders — well represented among MIT graduate students,” Sharp adds.

Sharp’s donation — matched by the Schimmel family gift — provides funds to establish fellowships for biology graduate students. “I hope others will join me in supporting the biology department and life sciences here at the Institute through the Schimmel family matching opportunity, as their investment will impact students and research for generations to come.”

Match point

“I am extremely grateful to Paul, his family, and Professor Phillip Sharp and Ann Sharp for their generosity and helping to inspire others to follow their lead,” says Biology Department Head and Praecis Professor of Biology Alan Grossman.

Grossman has worked with Sharp and the Schimmels for many years and is keenly aware of the important role these gifts play in a time of dwindling government investment in the sciences.

“As federal support for graduate training continues to wane over time, support from individuals like Paul, Phil, and others becomes crucial to the future of the life sciences,” Grossman says. “As COVID-19 has laid bare, there has never been a more critical need or better time to invest in basic science than right now.”

With $18 million remaining in available matching funds, Grossman says that he and partners throughout MIT continue to seek out others who wish to contribute to and participate in the Schimmel Family Program for Life Sciences. “Providing students with the resources they need to be successful in their education, research, and careers remains at the core of our mission,” says Grossman.

“Paul and the Schimmel family have provided other donors with an extraordinary opportunity to amplify the impact of their giving by leveraging their vision for the betterment of life sciences at the Institute,” says Biology Director of Development Daniel Griffin. “This initiative is resonating with people in and outside of the MIT community and we are all excited to see where it leads.”

Ensuring a bright future for Seattle
Ari Daniel | MIT Technology Review
January 10, 2022
In 1982, when Lynn Best ’69 joined the public utility Seattle City Light, her team faced an immediate challenge: evaluating the environmental, cultural, and financial impacts of its three dams generating electricity on the Skagit River in northwest Washington State. As acting director, she was able to persuade City Light to allow the environmental team to lead negotiations.

“Of course,” Best says, “the biggest issue was protecting the salmon on the river.” Four species of salmonids were known to spawn at different times and depths. The team relied upon science to determine optimal flow and ramping rates, placing the health of these species first, above power demand. Because the work was done in collaboration with state and federal agencies as well as local tribal communities, these partner groups signed on to the approach, which was the first time this had ever happened on a large hydro project. The fish responded immediately. The chum and pink salmon returned to historic abundances.

City Light’s efforts didn’t go unnoticed. In 1992, a senior member of the Federal Energy Regulatory Commission said that the utility’s Skagit River effort was the most comprehensive piece of work for the public good that he had ever seen. According to Best, if you dig hard enough, multiple science-based solutions to a problem emerge. And in her experience, at least one of these answers can benefit all the stakeholders. It’s a lesson she learned during her time as a biology major at MIT.

Of course, mistakes happen. Roughly a decade ago, the dam gates failed to open properly, draining water away from a number of salmon nests. This time, as environmental affairs director of Seattle City Light, Best and her now much larger team reported the violation to their partners. The tribal communities “didn’t advocate for any penalties, which is pretty unheard-of in those circumstances,” she says. It was a testament to how effective her cooperative approach had been.

In 2005, under Best’s leadership, Seattle City Light became the first utility in the nation to go carbon neutral. And more recently, during her time as the organization’s chief environmental officer, she championed an environmental justice program to protect and support diverse and economically disadvantaged communities.

Best retired from Seattle City Light in early 2020. She is now a commissioner on the Skagit Environmental Endowment Commission, dedicated to protecting the Upper Skagit environment on both sides of the border. She also spends time birdwatching and hiking. Her legacy of relationship building and environmental stewardship endures.

“Geeking Out” About Lab Equipment
Julie Fox | MIT Alumni Association
January 14, 2022

“Globally, there is a huge disparity in access to scientific resources,” says Melissa Wu ’05, CEO of Seeding Labs. “About 80 percent of the world’s population lives in low- and middle-income countries, but 80 percent of research funding is directed to just 10 countries in the world.”

And that disparity, she explains, biases what gets studied, what societal challenges are addressed, and who is a part of the scientific workforce. Seeding Labs is an NGO focused on shifting that balance of resources. It empowers scientists to transform the world by providing the resources needed to fight global diseases, feed a growing population, and protect our planet.

After leaving MIT, where she was active in service to her community and discovered a passion for research, Wu intended to pursue a career as a research professor. While earning a PhD in cell and developmental biology at Harvard and getting involved in a student group that advocated for global research, she began to realize that she craved more immediate impact from her work.

“As students, we convened a panel to talk about the barriers that are unique to researchers in lower- and middle-income countries,” she says. “We saw an immediate solution to one of those challenges: access to laboratory equipment, which can be prohibitively expensive. I worked with other students to collect and ship surplus equipment to researchers in developing countries. Within a few months after receiving equipment, we heard back the impacts of our efforts―successful grant apps, staff hired, and graduate students recruited to their labs.”

“After a few years of doing this work as students, we realized that we’d found a solution to a challenge that nobody else seemed able to address,” Wu explained. She supported a Harvard classmate, Nina Dudnik, in forming Seeding Labs, a nonprofit that could grow and sustain their efforts. Wu joined the Boston-based company as a full-time staff member in 2014 after completing her PhD, holding several positions before becoming CEO in 2019.

Seeding Labs aims to unleash the power of the most talented scientists around the globe to do what they do best: research, innovate, and discover. Through its flagship program, Instrumental Access, the company provides high-quality laboratory equipment at huge discounts to universities and research institutes in developing countries. “Since 2003, we’ve supported nearly 100 universities in 36 countries around the world with equipment that would’ve cost an estimated $40 million to obtain otherwise.”

Wu says that the stories of success, and the gratitude, from the universities and research institutions are continually inspiring. “One of the researchers told us that it was his dream to start a lab. There is no better reward than to be told that you’re fulfilling someone’s dream.” And in the current pandemic, these established labs are helping to save lives. In southeastern Africa, the Malawi University of Science and Technology has been able to analyze thousands of Covid-19 test kits in its lab because of the specialized equipment received through a 2017 Instrumental Access award.

The impact of the new equipment has a ripple effect beyond the labs and their research projects. Seeding Labs recently supported the top school in Tanzania, the Dar es Salaam University College of Education, with a shipment of lab equipment to help train the next generation of chemistry teachers. The university reported that the student dropout rate fell by 50 percent after receipt of the lab equipment, Wu says.

Her commitment to service doesn’t stop at helping to fund the global science community through lab equipment—Wu has also devoted her time to mentoring, with a special interest in helping to foster more diversity and equity in the sciences. “You know the phrase when you ‘geek out’ about something? It’s like you have this overwhelming desire to share something that you’re really passionate about because it makes you happy and you want others to experience that too. That’s what my love of scientific research is like. Who wouldn’t be excited about the opportunity to understand and then literally transform the world we live in? I want to share that with others and hope that I can inspire them to chase their dreams.”

Uncovering the mysteries of methylation in plants
Eva Frederick | Whitehead Institute
January 11, 2022

Growing up is a complex process for multi-celled organisms — plants included. In the days or weeks it takes to go from a seed to a sprout to a full plant, plants express hundreds of genes in different places at different times.

In order to conduct this symphony of genes, plants rely in part on an elegant regulatory method called DNA methylation. By adding or removing small molecules called methyl groups to the DNA strand, the plant can silence or activate different regions of its genetic code without changing the underlying sequence.

In a new paper from the lab of Whitehead Institute Member Mary Gehring, researchers led by former Gehring lab postdoc Ben Williams (now an assistant professor at the University of California, Berkeley) tease apart the role of proteins governing this system of genetic control, and reveal how enzymes that regulate methylation can affect essential decisions for plants such as when to produce flowers. “We’re starting to see that there is actually a broader role for  demethylation [in plant development] than we thought,” Gehring said.

In the model plant Arabidopsis thaliana, methylation is regulated in part by enzymes encoded by  a family of four genes called the DEMETER genes. The protein products of these genes are in charge of demethylating, or removing those methyl groups from the DNA, allowing different parts of the strand to be expressed. “You have these enzymes that can come in and completely change the way the DNA is read in different cells, which I find super interesting,” Williams said.

But teasing apart the role of each DEMETER gene has proved difficult in the past, because one member of this gene family in particular, called DME, is essential for seed development. Knock out DME, and the seed is aborted. “We had to design a synthetic gene to get around that,” Williams said. “We had to create plants that would rescue the reproductive failure, but then still be mutated throughout the rest of the life cycle.”

The researchers accomplished this by putting the DME gene under the control of a genetic element called a promoter that allowed it to be expressed in a cell that only existed in the plant during seed development. Once the plant was past the critical point where DME was needed for development, the gene would no longer be expressed, allowing the plant to grow up as a dme knockout. “It was an exciting thing, finally being able to create this knockout,” Gehring said.

Now, for the first time, the researchers could create plants with any combination of the DEMETER family genes knocked out, and then compare them to try and understand what the enzymes produced by each of the four genes was doing.

As expected, plants missing any of the DEMETER demethylases ended up with areas of their genomes with too many methyl groups (this is called hypermethylation). These areas were often overlapping, suggesting that the four DEMETER genes shared responsibility for demethylating certain areas of the genome.

“When one of these enzymes is gone, the others are surprisingly good at knowing that they need to step forward and do the job instead,” Williams said. “So the system has flexibility built in, which makes sense if it’s going to be involved in making important decisions like when to make flowers. You’d want there to be multiple layers of responsibility, right? It’s like in an organization, you don’t want to load all responsibility on one person — you’d want a few people who can take on that responsibility.”

Williams hypothesizes that while the DEMETER enzymes could step in for each other when needed, each specialized in demethylating DNA in particular types of plant tissue. “If you look at the protein sequences,they are actually really similar,” he said. “What’s different about them is they’re expressed in different cell types.”

A crucial finding of the study came about when the researchers knocked out all four genes in the DEMETER family at the same time. “All flowering plants have this really important decision of when to make flowers,” Williams said. “For plants out in the wild, that decision is usually dependent on temperature and pollinators. What we found really strange is that these mutants just flowered straight away. It’s almost like they weren’t even putting any effort into the decision. They made a few leaves, then boom, flower.”

When the researchers dove deeper, they saw that one area of the genome in particular that controls flowering time is under very careful and continuous regulation by methylating and demethylating enzymes. “We don’t really know why they’re doing that,” he said. “But when you knock out the demethylases, that gene just becomes methylated, and it’s then switched off. And that just sends plants into an automatic flowering state.”

In the future, the researchers plan to investigate other outcomes associated with their quadruple knockout of the DEMETER genes. “When we knocked out all four of the enzymes, it led to a lot of interesting phenotypes and tons of stuff to study,” Williams said. “We’ve learned through doing this that with DEMETER, like many gene families, we had to knock out all the players to find out the importance of what they are doing.”

Gehring will continue the research at Whitehead Institute. Williams recently started his own lab at the University of California, Berkeley. “I feel very lucky because this project has given me two or three different avenues that I can pursue in my new lab,” Williams said. “It has opened a lot of doors, which is very rewarding.”