It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Wednesday, December 13, 2023
NIH study shows how genes in retina get regulated during development
Genome topology map of human retina development lays foundation for understanding diverse clinical phenotypes in simple and complex eye diseases
THIS IMAGE METAPHORICALLY CAPTURES THE CELL DIFFERENTIATION PROCESS DURING RETINAL ORGANOID DEVELOPMENT. LOOSE YARN REPRESENTING UNCOMPACTED DNA IS WOUND BY A CROCHET HOOK AROUND BUTTONS REPRESENTING NUCLEOSOMES AND CULMINATING IN A TIGHTLY CONDENSED BALL OF CHROMATIN THAT FORMS AN EYE-LIKE SHAPE.
Researchers at the National Institutes of Health have mapped the 3D organization of genetic material of key developmental stages of human retinal formation, using intricate models of a retina grown in the lab. The findings lay a foundation for understanding clinical traits in many eye diseases, and reveal a highly dynamic process by which the architecture of chromatin, the DNA and proteins that form chromosomes, regulates gene expression. The findings were published in Cell Reports.
“These results provide insights into the heritable genetic landscape of the developing human retina, especially for the most abundant cell types that are commonly associated with vision impairment in retinal diseases,” said the study’s lead investigator, Anand Swaroop, Ph.D., chief of the Neurobiology, Neurodegeneration, and Repair Laboratory at the National Eye Institute (NEI), part of NIH.
Using deep Hi-C sequencing, a tool used for studying 3D genome organization, the researchers created a high-resolution map of chromatin in a human retinal organoid at five key points in development. Organoids are tissue models grown in a lab and engineered to replicate the function and biology of a specific type of tissue in a living body.
Genes, the sequences that code for RNA and proteins, are interspersed throughout long strands of DNA. Those DNA strands get packaged into chromatin fibers, which are spooled around histone proteins and then repeatedly looped to form highly compact structures that fit into the cell nucleus.
All those loops create millions of contact points where genes encounter non-coding DNA sequences, such as super enhancers, promoters, and silencers that regulate gene expression. Long considered “junk DNA”, these non-coding sequences are now recognized to play a crucial role in controlling which genes get expressed in a cell and when. Studies of chromatin’s 3D architecture shed light on how these non-coding regulatory elements exert control even when their location on a DNA strand is remote from the genes they regulate.
At each of the five key retinal organoid developmental stages, billions of chromatin contact point pairs were sequenced and analyzed.
The findings reveal a dynamic picture: Spatial organization of the genome within the nucleus is transformed during retinal development, facilitating expression of specific genes at key time periods. For example, at a stage when immature cells start developing retinal cell characteristics, chromatin contact points shift from a mostly proximal-enriched state to add more distal interactions.
There also appears to be a hierarchy of compartments that organize the contact point interactions. Some of these compartments, called “A” and “B”, are stable, but others swap during development, which further serves to enhance or inhibit gene expression.
“The datasets resulting from this research serve as a foundation for future investigations into how non-coding sections of the genome are relevant for understanding divergent phenotypes in single gene mutation (Mendelian) disorders, as well as complex retinal diseases,” Swaroop said.
The study was funded by the NEI Intramural Research Program (ZIAEY000450 and ZIAEY000546). NEI is part of the National Institutes of Health.
Reference:
Qu Z, Batz Z, Singh N, Marchal C, Swaroop A, “Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids”. Published December 2, 2023 in Cell https://doi.org/10.1016/j.celrep.2023.113543
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This press release describes a basic research finding. Basic research increases our understanding of human behavior and biology, which is foundational to advancing new and better ways to prevent, diagnose, and treat disease. Science is an unpredictable and incremental process— each research advance builds on past discoveries, often in unexpected ways. Most clinical advances would not be possible without the knowledge of fundamental basic research. To learn more about basic research, visit https://www.nih.gov/news-events/basic-research-digital-media-kit.
NEI leads the federal government’s efforts to eliminate vision loss and improve quality of life through vision research…driving innovation, fostering collaboration, expanding the vision workforce, and educating the public and key stakeholders. NEI supports basic and clinical science programs to develop sight-saving treatments and to broaden opportunities for people with vision impairment. For more information, visit https://www.nei.nih.gov.
About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov/.
THE RETINA OF VERTEBRATE SPECIES, SUCH AS MICE AND HUMANS, ARE REMARKABLY CONSERVED SINCE THE ORIGIN OF JAWED VERTEBRATES MORE THAN 400 MILLION YEARS AGO. THIS DIAGRAM SHOWS THE SIMILARITIES BETWEEN THE RETINAL CELLS OF HUMANS AND MICE, INCLUDING THE ON AND OFF “MIDGET” RETINAL GANGLION CELLS (MGCS).
CREDIT: ILLUSTRATION BY HUGO SALAIS, METAZOA STUDIO, SPAIN
Karthik Shekhar and his colleagues raised a few eyebrows as they collected cow and pig eyes from Boston butchers, but those eyes — eventually from 17 separate species, including humans — are providing insights into the evolution of the vertebrate retina and could lead to better animal models for human eye diseases.
The retina is a miniature computer containing diverse types of cells that collectively process visual information before transmitting it to the rest of the brain. In a comparative analysis across animals of the many cell types in the retina — mice alone have 130 types of cells in the retina, as Shekhar’s previous studies have shown — the researchers concluded that most cell types have an ancient evolutionary history. These cell types, distinguished by their differences at the molecular level, give clues to their functions and how they participate in building our visual world.
Their remarkable conservation across species suggests that the retina of the last common ancestor of all mammals, which roamed the earth some 200 million year ago, must have had a complexity rivaling the retina of modern mammals. In fact, there are clear hints that some of these cell types can be traced back more than 400 million years ago to the common ancestors of all vertebrates — that is, mammals, reptiles, birds and jawed fish.
The results will be published Dec. 13 in the journal Nature as part of a 10-paper package reporting the latest results of the BRAIN Initiative Cell Census Network's efforts to create a cell-type atlas of the adult mouse brain. The first author is Joshua Hahn, a chemical and biomolecular engineering graduate student in Shekhar’s group at the University of California, Berkeley. The work was an equal collaboration with the group of Joshua Sanes at Harvard University.
The findings were a surprise, since vertebrate vision varies so widely from species to species. Fish need to see underwater, mice and cats require good night vision, monkeys and humans evolved very sharp daytime eyesight for hunting and foraging. Some animals see vivid colors, while others are content with seeing the world in black and white.
Yet, numerous cell types are shared across a range of vertebrate species, suggesting that the gene expression programs that define these types likely trace back to the common ancestor of jawed vertebrates, the researchers concluded.
The team found, for example, that one cell type — the “midget” retinal ganglion cell — that is responsible for our ability to see fine detail, is not unique to primates, as it was thought to be. By analyzing large-scale gene expression data using statistical inference approaches, the researchers discovered evolutionary counterparts of midget cells in all other mammals, though these counterparts occurred in much smaller proportions.
“What we are seeing is that something thought to be unique to primates is clearly not unique. It’s a remodeled version of a cell type that is probably very ancient," said Shekhar, a UC Berkeley assistant professor of chemical and biomolecular engineering. "The early vertebrate retina was probably extremely sophisticated, but the parts list has been used, expanded, repurposed or refurbished in all the species that have descended since then."
Coincidentally, one of Shekhar's UC Berkeley colleagues, Teresa Puthussery of the School of Optometry, reported last month in Nature that another cell type thought to have been lost in the human eye — a type of retinal ganglion cell responsible for gaze stabilization — is still there. Puthussery and her colleagues used information from a previous paper co-authored by Shekhar to select molecular markers that helped identify this cell type in primate retinal tissue samples.
The discoveries are, in a sense, not a total surprise, since the eyes of vertebrates have a similar plan: Light is detected by photoreceptors, which relay the signal to bipolar, horizontal and amacrine cells, which in turn connect with retinal ganglion cells, which then relay the results to the brain's visual cortex. Shekhar uses new technologies, in particular single-cell genomics, to assay the molecular composition of thousands to tens of thousands of neurons at once within the visual system, from the retina to the visual cortex.
Because the number of identified retinal cell types varies widely in vertebrates — about 70 in humans, but 130 in mice, based on previous studies by Shekhar and his colleagues — the origins of these diverse cell types were a mystery.
One possibility that emerged from the new research, Shekhar said, is that as the primate brain became more complex, primates began to rely less on signal processing within the eye — which is key to reflexive actions, such as reacting to an approaching predator — and more on analysis within the visual cortex. Hence the apparent decrease in molecularly distinct cell types in the human eye.
"Our study is saying that the human retina may have evolved to trade cell types that perform sophisticated visual computations for cell types that basically just transmit a relatively unprocessed image of the visual world with the brain so that we can do a lot more sophisticated things with that," Shekhar said. "We are giving up speed for finesse."
The team's new detailed map of cell types in a variety of vertebrate retinas could aid research on human eye disease. Shekhar’s group is also studying molecular hallmarks of glaucoma, the leading cause of irreversible blindness in the world and, in the U.S., the second most common cause of blindness after macular degeneration.
Yet, while mice are a favorite model animal for studying glaucoma, they have very few of the midget retinal ganglion cell counterparts. These cell types make up only 2% to 4% of all ganglion cells in mice, whereas 90% of retinal ganglion cells are midget cells in humans.
"This work is clinically important because, ultimately, the midget cells are probably what we should care about the most in human glaucoma," Shekhar said. "Knowing their counterparts in the mouse will hopefully help us design and interpret these glaucoma mouse models a little better."
Single-cell transcriptomics
Shekhar and Sanes have, for the past eight years, been applying single-cell genomic approaches to profile the mRNA molecules in cells to categorize them according to their gene expression fingerprints. That technique has gradually helped identify more and more distinct cell types within the retina, many of them through studies that Shekhar initiated while a postdoctoral fellow with Aviv Regev, one of the pioneers of single-cell genomics, at the Broad Institute. It was in her lab that Shekhar began working with Sanes, a renowned retinal neurobiologist who became Shekhar’s co-advisor and collaborator.
In the current study, they wanted to expand their single-cell transcriptomic approach to other species to understand how retinal cell types have changed through evolution. They gathered, in all, eyes from 17 species: human, two monkeys (macaque and marmoset), four rodents (three species of mice and one ground squirrel), three ungulates (cow, sheep and pig), tree shrew, opossum, ferret, chicken, lizard, zebrafish and lamprey.
With Sanes' team at Harvard conducting the transcriptomic experiments and Shekhar's team at UC Berkeley conducting the computational analysis, many new cell types were identified in each of the species. They then mapped this variety to a smaller set of "orthotypes" — cell types that have likely descended from the same ancestral cell type in early vertebrates.
For bipolar cells, which are a class of neurons that lie between the photoreceptors and retinal ganglion cells, they found 14 distinct orthotypes. Most extant species contain 13 to 16 bipolar types, suggesting that these types have evolved little. In contrast, they found 21 orthotypes of retinal ganglion cells, which exhibit greater variation among species. Studies thus far have identified more than 40 distinct types in mice and about 20 different types in humans.
Interestingly, the pronounced evolutionary divergence among types of retinal ganglion cells, as compared to other retinal classes, suggests that natural selection acts more strongly on diversifying neuron types that transmit information from the retina to the rest of the brain.
They also found that numerous transcription factors, which have been implicated in retinal cell type specification in mice, are highly conserved, suggesting that the molecular steps leading to the development of the retina might be evolutionarily conserved, as well.
Based on the new work, Shekhar is refocusing his glaucoma research on the analogs of midget cells, called alpha cells, in mice.
The work was supported primarily by the National Institutes of Health (K99EY033457, R00EY028625, R21EY028633, U01MH105960, T32GM007103), the Chan-Zuckerberg Initiative (CZF-2019-002459) and the Glaucoma Research Foundation (CFC4). Shekhar also acknowledges support from the Hellman Fellows Program. Sanes was funded in part by NIH’s Brain Research Through Advancing Innovative Neurotechnologies Initiative, or the BRAIN Initiative.
JOURNAL
Nature
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
Animals
ARTICLE TITLE
Evolution of neuronal cell classes and types in the vertebrate retina
ARTICLE PUBLICATION DATE
13-Dec-2023
Machine learning sees into the future to prevent sight loss in humans
MYOPIC MACULOPATHY, ALSO KNOWN AS MYOPIC MACULAR DEGENERATION, IS A KEY FEATURE OF PATHOLOGIC MYOPIA. IN THE META-PM CLASSIFICATION SYSTEM, MYOPIC MACULOPATHY LESIONS ARE CATEGORIZED INTO FIVE CATEGORIES FROM NO MYOPIC RETINAL LESIONS (CATEGORY 0), TESSELLATED FUNDUS ONLY (CATEGORY 1, FIGURE 1A), DIFFUSE CHORIORETINAL ATROPHY (CATEGORY 2, FIGURE 1B&C), PATCHY CHORIORETINAL ATROPHY (CATEGORY 3, FIGURE 1D ARROWS), TO MACULAR ATROPHY (CATEGORY 4, FIGURE 1E&F).
CREDIT: DEPARTMENT OF OPHTHALMOLOGY AND VISUAL SCIENCE, TMDU
Researchers from Tokyo Medical and Dental University (TMDU) develop models based on machine learning that predict long-term visual acuity in patients with high myopia, one of the top three causes of irreversible blindness in many regions of the world
Tokyo, Japan – Machine learning has been found to predict well the outcomes of many health conditions. Now, researchers from Japan have found a way to predict whether people with severe shortsightedness will have good or bad vision in the future.
In a study recently published in JAMA Ophthalmology, researchers from the Tokyo Medical and Dental University (TMDU) developed a machine-learning model that works well for predicting—and visualizing—the risk of visual impairment over the long term.
People with extreme shortsightedness (called high myopia) can clearly see objects that are near to them but cannot focus on objects at a distance. Contacts, glasses, or surgery can be used to correct their vision, but having high myopia is not just inconvenient; half of the time it leads to a condition called pathologic myopia, and complications from pathologic myopia are the leading causes of blindness.
“We know that machine-learning algorithms work well on tasks such as identifying changes and complications in myopia,” says Yining Wang, lead author of the study, “but in this study, we wanted to investigate something different, namely how good these algorithms are at long-term predictions.”
To do this, the team performed a cohort study and looked at the visual acuity of 967 Japanese patients at TDMU’s Advanced Clinical Center for Myopia after 3 and 5 years had passed. They formed a dataset from 34 variables that are commonly collected during ophthalmic examinations, such as age, current visual acuity, and the diameter of the cornea. They then tested several popular machine-learning models such as random forests and support vector machines. Of these models, the logistic regression-based model performed the best at predicting visual impairment at 5 years.
However, predicting outcomes is only part of the story. “It’s also important to present the model’s output in a way that is easy for patients to understand and convenient for making clinical decisions,” says Kyoko Ohno-Matsui, senior author. To do this, the researchers used a nomogram to visualize the classification model. Each variable is assigned a line with a length that indicates how important it is for predicting visual acuity. These lengths can be converted into points that can be added up to obtain a final score explaining the risk of visual impairment in future.
People who permanently lose their vision often suffer both financially and physically as a result of their loss of independence. The decrease in global productivity caused by severe visual impairment was estimated to be USD94.5 billion in 2019. Although the model still has to be evaluated on a wider population, this study has shown that machine-learning models have good potential to help address this increasingly important public health concern, which will benefit both individuals and society as a whole.
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The article, “Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes,” was published in JAMA Ophthalmology at DOI: 10.1001/jamaophthalmol.2023.4786
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