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Visualizing The Placenta, a Critical but Poorly Understood Organ

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Ultrasound image of placenta
Ultrasound images showing fetal (left) and maternal (right) placental vasculature. Credit: Eastern Virginia Medical School and University of Texas Medical Branch

The placenta is the Rodney Dangerfield of organs; it gets no respect, no respect at all. This short-lived but critical organ supports pregnancy by bringing nutrients and oxygen to the fetus, removing waste, providing immune protection, and producing hormones to support fetal development.

It also influences the lifelong health of both mother and child. Problems with the placenta can lead to preeclampsia, gestational diabetes, poor fetal growth, preterm birth, and stillbirth. Although we were all connected to one, the placenta is the least understood, and least studied, of all human organs.

What we do know about the human placenta largely comes from studying it after delivery. But that’s like studying the heart after it’s stopped beating. It doesn’t help us predict complications in time to avert a crisis.

To fill these knowledge gaps, NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) developed the Human Placenta Project (HPP) to noninvasively study the placenta during pregnancy. Since 2014, this approximately $88 million collaborative research effort has been developing ultrasound, magnetic resonance imaging (MRI), and blood-based biomarker methods to study how the placenta functions in real time and in greater detail.

As illustrated in the image above, advanced ultrasound tools allowed HPP researchers at Eastern Virginia Medical School, Norfolk, and the University of Texas Medical Branch, Galveston, to gain a detailed look at the placenta’s intricate arrangement of blood vessels, or vasculature. By evaluating both fetal (left panel) and maternal (right panel) placental vasculature in 610 pregnant people starting at 13 weeks of gestation, the investigators aimed to identify early changes that predicted later complications.

They observed that such changes can start in the first trimester and affect both the vasculature and placental tissue. While further research is needed, these findings suggest that placental ultrasound monitoring can inform efforts to prevent and treat pregnancy complications.

Another HPP team led by Boston Children’s Hospital is developing an MRI strategy to monitor blood flow and oxygen transport through the placenta during pregnancy. Interpreting and visualizing MRI data of the placenta is challenging because of its variable shape, the tendency of muscles in the uterus to begin tightening or contracting well before labor [1], and other factors.

As shown in the video above, the researchers developed a way to account for the motion of the uterus and “freeze” the placenta to make it easier to study (left two panels of video) [2]. They also developed algorithms to better visualize the complex patterns of placental oxygen content during contractions (center panel) [3]. The scientists then carried out initial visualizations of blood flow through the placenta shortly after delivery (second panel from right) [4].

They now intend to map these MRI findings to the placenta itself after delivery (far right panel), which will allow them to explore how additional factors such as gene expression patterns and genetic variants contribute to placental function. Ultimately, they plan to apply these MRI techniques to monitor the placenta in real time during pregnancy and identify changes that indicate compromised function early enough to adjust maternal management as needed.

Other HPP efforts focus on identifying components in maternal blood that reflect the status of the placenta. For example, an HPP research team led by scientists at the University of California, Los Angeles, adapted non-invasive prenatal testing methods to analyze genetic material shed from the placenta into the maternal bloodstream. Their findings suggest that distinctive patterns in this genetic material detected early in pregnancy may indicate risk for later complications [5].

Another HPP team, led by investigators at Columbia University, New York, helped establish that extracellular RNAs (exRNAs) released by the placenta into maternal circulation reflect the placenta’s status at a cellular level beginning in the first trimester. To harness the potential of exRNA biomarkers, the investigators are optimizing methods to isolate, sequence, and analyze exRNAs in maternal blood.

These are just a few examples of the cutting-edge work being funded through the HPP, which complements NICHD’s longstanding investment in basic research to unravel the physiology of and real-time gene expression in the placenta. Unlocking the secrets of the placenta may one day help us to prevent and treat a range of common pregnancy complications, while also providing insights into other areas of science and medicine such as cardiovascular disease and aging. NICHD is committed to giving this important organ the respect it deserves.

References:

[1] Placental MRI: Effect of maternal position and uterine contractions on placental BOLD MRI measurements. Abaci Turk E, Abulnaga SM, Luo J, Stout JN, Feldman H, Turk A, Gagoski B, Wald LL, Adalsteinsson E, Roberts DJ, Bibbo C, Robinson JN, Golland P, Grant PE, Barth, Jr WH. Placenta. 2020 Jun 1; 95: 69-77.

[2] Spatiotemporal alignment of in utero BOLD-MRI series. Turk EA, Luo J, Gagoski B, Pascau J, Bibbo C, Robinson JN, Grant PE, Adalsteinsson E, Golland P, Malpica N. J Magn Reson Imaging. 2017 Aug;46(2):403-412.

[3] Volumetric parameterization of the placenta to a flattened template. Abulnaga SM, Turk EA, Bessmeltsev M, Grant PE, Solomon J, Golland P. IEEE transactions on medical imaging. 2022 April;41(4):925-936.

[4] Placental MRI: development of an MRI compatible ex vivo system for whole placenta dual perfusion. Stout JN, Rouhani S, Turk EA, Ha CG, Luo J, Rich K, Wald LL, Adalsteinsson E, Barth, Jr WH, Grant PE, Roberts DJ. Placenta. 2020 Nov 1; 101: 4-12.

[5] Cell-free DNA methylation and transcriptomic signature prediction of pregnancies with adverse outcomes. Del Vecchio G, Li Q, Li W, Thamotharan S, Tosevska A, Morselli M, Sung K, Janzen C, Zhou X, Pellegrini M, Devaskar SU. Epigenetics. 2021 Jun;16(6):642-661. 

Links:

Human Placenta Project (Eunice Kennedy Shriver National Institute of Child Health and Human Development/NIH)

Preeclampsia (NICHD)

Understanding Gestational Diabetes (NICHD)

Preterm Labor and Birth (NICHD)

Stillbirth (NICHD)

Abuhamad Project Information (NIH RePORTER)

Grant Project Information (NIH RePORTER)

Devaskar Project Information (NIH RePORTER)

Williams Project Information (NIH RePORTER)

Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 10th in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.


Millions of Single-Cell Analyses Yield Most Comprehensive Human Cell Atlas Yet

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A field of playing cards showing different body tissues

There are 37 trillion or so cells in our bodies that work together to give us life. But it may surprise you that we still haven’t put a good number on how many distinct cell types there are within those trillions of cells.

That’s why in 2016, a team of researchers from around the globe launched a historic project called the Human Cell Atlas (HCA) consortium to identify and define the hundreds of presumed distinct cell types in our bodies. Knowing where each cell type resides in the body, and which genes each one turns on or off to create its own unique molecular identity, will revolutionize our studies of human biology and medicine across the board.

Since its launch, the HCA has progressed rapidly. In fact, it has already reached an important milestone with the recent publication in the journal Science of four studies that, together, comprise the first multi-tissue drafts of the human cell atlas. This draft, based on analyses of millions of cells, defines more than 500 different cell types in more than 30 human tissues. A second draft, with even finer definition, is already in the works.

Making the HCA possible are recent technological advances in RNA sequencing. RNA sequencing is a topic that’s been mentioned frequently on this blog in a range of research areas, from neuroscience to skin rashes. Researchers use it to detect and analyze all the messenger RNA (mRNA) molecules in a biological sample, in this case individual human cells from a wide range of tissues, organs, and individuals who voluntarily donated their tissues.

By quantifying these RNA messages, researchers can capture the thousands of genes that any given cell actively expresses at any one time. These precise gene expression profiles can be used to catalogue cells from throughout the body and understand the important similarities and differences among them.

In one of the published studies, funded in part by the NIH, a team co-led by Aviv Regev, a founding co-chair of the consortium at the Broad Institute of MIT and Harvard, Cambridge, MA, established a framework for multi-tissue human cell atlases [1]. (Regev is now on leave from the Broad Institute and MIT and has recently moved to Genentech Research and Early Development, South San Francisco, CA.)

Among its many advances, Regev’s team optimized single-cell RNA sequencing for use on cell nuclei isolated from frozen tissue. This technological advance paved the way for single-cell analyses of the vast numbers of samples that are stored in research collections and freezers all around the world.

Using their new pipeline, Regev and team built an atlas including more than 200,000 single-cell RNA sequence profiles from eight tissue types collected from 16 individuals. These samples were archived earlier by NIH’s Genotype-Tissue Expression (GTEx) project. The team’s data revealed unexpected differences among cell types but surprising similarities, too.

For example, they found that genetic profiles seen in muscle cells were also present in connective tissue cells in the lungs. Using novel machine learning approaches to help make sense of their data, they’ve linked the cells in their atlases with thousands of genetic diseases and traits to identify cell types and genetic profiles that may contribute to a wide range of human conditions.

By cross-referencing 6,000 genes previously implicated in causing specific genetic disorders with their single-cell genetic profiles, they identified new cell types that may play unexpected roles. For instance, they found some non-muscle cells that may play a role in muscular dystrophy, a group of conditions in which muscles progressively weaken. More research will be needed to make sense of these fascinating, but vital, discoveries.

The team also compared genes that are more active in specific cell types to genes with previously identified links to more complex conditions. Again, their data surprised them. They identified new cell types that may play a role in conditions such as heart disease and inflammatory bowel disease.

Two of the other papers, one of which was funded in part by NIH, explored the immune system, especially the similarities and differences among immune cells that reside in specific tissues, such as scavenging macrophages [2,3] This is a critical area of study. Most of our understanding of the immune system comes from immune cells that circulate in the bloodstream, not these resident macrophages and other immune cells.

These immune cell atlases, which are still first drafts, already provide an invaluable resource toward designing new treatments to bolster immune responses, such as vaccines and anti-cancer treatments. They also may have implications for understanding what goes wrong in various autoimmune conditions.

Scientists have been working for more than 150 years to characterize the trillions of cells in our bodies. Thanks to this timely effort and its advances in describing and cataloguing cell types, we now have a much better foundation for understanding these fundamental units of the human body.

But the latest data are just the tip of the iceberg, with vast flows of biological information from throughout the human body surely to be released in the years ahead. And while consortium members continue making history, their hard work to date is freely available to the scientific community to explore critical biological questions with far-reaching implications for human health and disease.

References:

[1] Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Segrè AV, Aguet F, Rozenblatt-Rosen O, Ardlie KG, Regev A, et al. Science. 2022 May 13;376(6594):eabl4290.

[2] Cross-tissue immune cell analysis reveals tissue-specific features in humans. Domínguez Conde C, Xu C, Jarvis LB, Rainbow DB, Farber DL, Saeb-Parsy K, Jones JL,Teichmann SA, et al. Science. 2022 May 13;376(6594):eabl5197.

[3] Mapping the developing human immune system across organs. Suo C, Dann E, Goh I, Jardine L, Marioni JC, Clatworthy MR, Haniffa M, Teichmann SA, et al. Science. 2022 May 12:eabo0510.

Links:

Ribonucleic acid (RNA) (National Human Genome Research Institute/NIH)

Studying Cells (National Institute of General Medical Sciences/NIH)

Human Cell Atlas

Regev Lab (Broad Institute of MIT and Harvard, Cambridge, MA)

NIH Support: Common Fund; National Cancer Institute; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; National Institute on Drug Abuse; National Institute of Mental Health; National Institute on Aging; National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Eye Institute


New Clues to Delta Variant’s Spread in Studies of Virus-Like Particles

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About 70,000 people in the United States are diagnosed with COVID-19 each and every day. It’s clear that these new cases are being driven by the more-infectious Delta variant of SARS-CoV-2, the novel coronavirus that causes COVID-19. But why does the Delta variant spread more easily than other viral variants from one person to the next?

Now, an NIH-funded team has discovered at least part of Delta’s secret, and it’s not all attributable to those widely studied mutations in the spike protein that links up to human cells through the ACE2 receptor. It turns out that a specific mutation found within the N protein coding region of the Delta genome also enables the virus to pack more of its RNA code into the infected host cell. As a result, there is increased production of fully functional new viral particles, which can go on to infect someone else.

This finding, published in the journal Science [1], comes from the lab of Nobel laureate Jennifer Doudna at the Howard Hughes Medical Institute, the Gladstone Institutes, San Francisco, and the Innovative Genomics Institute at the University of California, Berkeley. Co-leading the team was Melanie Ott, Gladstone Institutes.

The Doudna and Ott teams have developed an exciting new tool to study variants of the coronavirus. It’s a lab construct called a virus-like particle (VLP). These specially made VLPs have all the structural proteins of SARS-CoV-2 (shown above), but they contain no genetic material. Consequently, they are non-infectious replicas of the real virus that can be studied safely in any lab. Scientists don’t have to reserve time in labs equipped with heightened levels of biosafety, as is required when working with whole virus.

The VLPs also allow researchers to explore changes found in the coronavirus’s other essential proteins, not just the spike protein on its surface. In fact, all of the SARS-CoV-2 variants of concern, as defined by the World Health Organization (WHO), carry at least one mutation within the same stretch of seven amino acids in a viral protein known as the nucleocapsid (N protein). This protein, which hasn’t been widely studied, is required for the virus to make more of itself. It is also involved in the virus’s ability to package and release infectious RNA.

In the Science paper, Doudna and colleagues took a closer look at the N protein. They did so by developing a special system that used VLPs to package and deliver viral RNA messages into human cells.

Here’s how it works: The VLPs include all four of SARS-CoV-2’s structural proteins, including the spike and N proteins. In addition, they contain the RNA sequence that allows the virus to recognize its genetic material within the cell, so that it can be packaged into the next generation of viral particles.

Though the particles look just like SARS-CoV-2 from the outside, they lack the vast majority of the viral genome on the inside. But they do have one other key component: a snippet of RNA that makes cells invaded by VLPs glow. In fact, the more RNA messages a VLP delivers, the brighter the cells will glow. It allowed the researchers to spot successful invasions, while also quantifying the amount of RNA a particular VLP packed into a cell.

The researchers then produced SARS-CoV-2 VLPs including four mutations that are universally found within the N proteins of more transmissible variants of concern. That’s when they discovered those variants produced and delivered 10 times more RNA messages into cells.

The increased RNA also fits with what has been observed in people infected with the Delta variant. They produce about 10 times more virus in their nose and throat compared to people infected with the older variants.

But did those findings match what happens in the real virus? To find out, the researchers and their colleagues tested the N protein mutation found in the Delta variant in a high-level biosafety lab. And, indeed, their studies showed that the mutated virus within infected human lung cells produced about 50 times more infectious virus compared to the original SARS-CoV-2 variant.

The findings suggest that the N protein could be an important new target for effective COVID-19 therapeutics, and that tracking newly emerging mutations in the N protein might also be important for identifying new viral variants of concern. This new system is a powerful tool, and one that can also be used for exploring how newly arising variants in the future might affect the course of this terrible pandemic.

Reference:

[1] Rapid assessment of SARS-CoV-2 evolved variants using virus-like particles. Syed AM, Taha TY, Tabata T, Chen IP, Ciling A, Khalid MM, Sreekumar B, Chen PY, Hayashi JM, Soczek KM, Ott M, Doudna JA. Science. 2021 Nov 4:eabl6184.

Links:

COVID-19 Research (NIH)

Doudna Lab

NIH Support: National Institute of Allergy and Infectious Diseases


Artificial Intelligence Accurately Predicts RNA Structures, Too

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A mechanical claw grabs molecular models
Credit: Camille L.L. Townshend

Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.

This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.

Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.

In hopes of moving the field forward, a team led by Stephan Eismann and Raphael Townshend in the lab of Ron Dror, Stanford University, Palo Alto, CA, looked to a machine learning approach known as deep learning. It is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened.

One of the things that makes deep learning so powerful is it doesn’t rely on any preconceived notions. It also can pick up on important features and patterns that humans can’t possibly detect. But, as successful as this approach has been in solving many different kinds of problems, it has primarily been applied to areas of biology, such as protein folding, in which lots of data were available for researchers to train the computers.

That’s not the case with RNA molecules. To work around this problem, Dror’s team designed a neural network they call ARES. (No, it’s not the Greek god of war. It’s short for Atomic Rotationally Equivariant Scorer.)

To start, the researchers trained ARES on just 18 small RNA molecules for which structures had been experimentally determined. They gave ARES these structural models specified only by their atomic structure and chemical elements.

The next test was to see if ARES could determine from this small training set the best structural model for RNA sequences it had never seen before. The researchers put it to the test with RNA molecules whose structures had been determined more recently.

ARES, however, doesn’t come up with the structures itself. Instead, the researchers give ARES a sequence and at least 1,500 possible 3D structures it might take, all generated using another computer program. Based on patterns in the training set, ARES scores each of the possible structures to find the one it predicts is closest to the actual structure. Remarkably, it does this without being provided any prior information about features important for determining RNA shapes, such as nucleotides, steric constraints, and hydrogen bonds.

It turns out that ARES consistently outperforms humans and all other previous methods to produce the best results. In fact, it outperformed at least nine other methods to come out on top in a community-wide RNA-puzzles contest. It also can make predictions about RNA molecules that are significantly larger and more complex than those upon which it was trained.

The success of ARES and this deep learning approach will help to elucidate RNA molecules with potentially important implications for health and disease. It’s another compelling example of how deep learning promises to solve many other problems in structural biology, chemistry, and the material sciences when—at the outset—very little is known.

Reference:

[1] Geometric deep learning of RNA structure. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Science. 2021 Aug 27;373(6558):1047-1051.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (National Institute of General Medical Sciences/NIH)

RNA Biology (NIH)

RNA Puzzles

Dror Lab (Stanford University, Palo Alto, CA)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Single-Cell Study Offers New Clue into Causes of Cystic Fibrosis

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Healthy airways (left) show well-defined layers of ciliated cells (green) and basal stem cells (red). In airways affected by cystic fibrosis (right), the layers are disrupted, and a transitioning cell type (red and green in the same cell).
Credit: Carraro G, Nature, 2021

More than 30 years ago, I co-led the Michigan-Toronto team that discovered that cystic fibrosis (CF) is caused by an inherited misspelling in the cystic fibrosis transmembrane conductance regulator (CFTR) gene [1]. The CFTR protein’s normal function on the surface of epithelial cells is to serve as a gated channel for chloride ions to pass in and out of the cell. But this function is lost in individuals for whom both copies of CFTR are misspelled. As a consequence, water and salt get out of balance, leading to the production of the thick mucus that leaves people with CF prone to life-threatening lung infections.

It took three decades, but that CFTR gene discovery has now led to the development of a precise triple drug therapy that activates the dysfunctional CFTR protein and provides major benefit to most children and adults with CF. But about 10 percent of individuals with CF have mutations that result in the production of virtually no CFTR protein, which means there is nothing for current triple therapy to correct or activate.

That’s why more basic research is needed to tease out other factors that contribute to CF and, if treatable, could help even more people control the condition and live longer lives with less chronic illness. A recent NIH-supported study, published in the journal Nature Medicine [2], offers an interesting basic clue, and it’s visible in the image above.

The healthy lung tissue (left) shows a well-defined and orderly layer of ciliated cells (green), which use hair-like extensions to clear away mucus and debris. Running closely alongside it is a layer of basal cells (outlined in red), which includes stem cells that are essential for repairing and regenerating upper airway tissue. (DNA indicating the position of cell is stained in blue).

In the CF-affected airways (right), those same cell types are present. However, compared to the healthy lung tissue, they appear to be in a state of disarray. Upon closer inspection, there’s something else that’s unusual if you look carefully: large numbers of a third, transitional cell subtype (outlined in red with green in the nucleus) that combines properties of both basal stem cells and ciliated cells, which is suggestive of cells in transition. The image below more clearly shows these cells (yellow arrows).

Photomicroscopy showing red basal cells below green ciliated cells, with transitional cells between showing green centers and red outlines
Credit: Carraro G, Nature, 2021

The increased number of cells with transitional characteristics suggests an unsuccessful attempt by the lungs to produce more cells capable of clearing the mucus buildup that occurs in airways of people with CF. The data offer an important foundation and reference for continued study.

These findings come from a team led by Kathrin Plath and Brigitte Gomperts, University of California, Los Angeles; John Mahoney, Cystic Fibrosis Foundation, Lexington, MA; and Barry Stripp, Cedars-Sinai, Los Angeles. Together with their lab members, they’re part of a larger research team assembled through the Cystic Fibrosis Foundation’s Epithelial Stem Cell Consortium, which seeks to learn how the disease changes the lung’s cellular makeup and use that new knowledge to make treatment advances.

In this study, researchers analyzed the lungs of 19 people with CF and another 19 individuals with no evidence of lung disease. Those with CF had donated their lungs for research in the process of receiving a lung transplant. Those with healthy lungs were organ donors who died of other causes.

The researchers analyzed, one by one, many thousands of cells from the airway and classified them into subtypes based on their distinctive RNA patterns. Those patterns indicate which genes are switched on or off in each cell, as well as the degree to which they are activated. Using a sophisticated computer-based approach to sift through and compare data, the team created a comprehensive catalog of cell types and subtypes present in healthy airways and in those affected by CF.

The new catalogs also revealed that the airways of people with CF had alterations in the types and proportions of basal cells. Those differences included a relative overabundance of cells that appeared to be transitioning from basal stem cells into the specialized ciliated cells, which are so essential for clearing mucus from the lungs.

We are not yet at our journey’s end when it comes to realizing the full dream of defeating CF. For the 10 percent of CF patients who don’t benefit from the triple-drug therapy, the continuing work to find other treatment strategies should be encouraging news. Keep daring to dream of breathing free. Through continued research, we can make the story of CF into history!

References:

[1] Identification of the cystic fibrosis gene: chromosome walking and jumping. Rommens JM, Iannuzzi MC, Kerem B, Drumm ML, Melmer G, Dean M, Rozmahel R, Cole JL, Kennedy D, Hidaka N, et al. Science.1989 Sep 8;245(4922):1059-65.

[2] Transcriptional analysis of cystic fibrosis airways at single-cell resolution reveals altered epithelial cell states and composition. Carraro G, Langerman J, Sabri S, Lorenzana Z, Purkayastha A, Zhang G, Konda B, Aros CJ, Calvert BA, Szymaniak A, Wilson E, Mulligan M, Bhatt P, Lu J, Vijayaraj P, Yao C, Shia DW, Lund AJ, Israely E, Rickabaugh TM, Ernst J, Mense M, Randell SH, Vladar EK, Ryan AL, Plath K, Mahoney JE, Stripp BR, Gomperts BN. Nat Med. 2021 May;27(5):806-814.

Links:

Cystic Fibrosis (National Heart, Lung, and Blood Institute/NIH)

Kathrin Plath (University of California, Los Angeles)

Brigitte Gomperts (UCLA)

Stripp Lab (Cedars-Sinai, Los Angeles)

Cystic Fibrosis Foundation (Lexington, MA)

Epithelial Stem Cell Consortium (Cystic Fibrosis Foundation, Lexington, MA)

NIH Support: National Heart, Lung, and Blood Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Cancer Institute; National Center for Advancing Translational Sciences


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