Findings could lead to deeper understanding of why we age, and to medical laboratory tests and treatments to slow or even reverse aging
Can humans control aging by keeping their genes long and balanced? Researchers at Northwestern University in Evanston, Illinois, believe it may be possible. They have unveiled a “previously unknown mechanism” behind aging that could lead to medical interventions to slow or even reverse aging, according to a Northwestern news release.
Should additional studies validate these early findings, this line of testing may become a new service clinical laboratories could offer to referring physicians and patients. It would expand the test menu with assays that deliver value in diagnosing the aging state of a patient, and which identify the parts of the transcriptome that are undergoing the most alterations that reduce lifespan.
It may also provide insights into how treatments and therapies could be implemented by physicians to address aging.
“I find it very elegant that a single, relatively concise principle seems to account for nearly all of the changes in activity of genes that happen in animals as they change,” Thomas Stoeger, PhD, postdoctoral scholar in the Amaral Lab who led the study, told GEN. Clinical laboratories involved in omics research may soon have new anti-aging diagnostic tests to perform. (Photo copyright: Amaral Lab.)
Possible ‘New Instrument’ for Biological Testing
Researchers found clues to aging in the length of genes. A gene transcript length reveals “molecular-level changes” during aging: longer genes relate to longer lifespans and shorter genes suggest shorter lives, GEN summarized.
The phenomenon the researchers uncovered—which they dubbed transcriptome imbalance—was “near universal” in the tissues they analyzed (blood, muscle, bone, and organs) from both humans and animals, Northwestern said.
According to the National Human Genome Research Institute fact sheet, a transcriptome is “a collection of all the gene readouts (aka, transcript) present in a cell” shedding light on gene activity or expression.
The Northwestern study suggests “systems-level” changes are responsible for aging—a different view than traditional biology’s approach to analyzing the effects of single genes.
“We have been primarily focusing on a small number of genes, thinking that a few genes would explain disease,” said Luis Amaral, PhD, Senior Author of the Study and Professor of Chemical and Biological Engineering at Northwestern, in the news release.
“So, maybe we were not focused on the right thing before. Now that we have this new understanding, it’s like having a new instrument. It’s like Galileo with a telescope, looking at space. Looking at gene activity through this new lens will enable us to see biological phenomena differently,” Amaral added.
In their Nature Aging paper, Amaral and his colleagues wrote, “We hypothesize that aging is associated with a phenomenon that affects the transcriptome in a subtle but global manner that goes unnoticed when focusing on the changes in expression of individual genes.
“We show that transcript length alone explains most transcriptional changes observed with aging in mice and humans,” they continued.
In tissues studied, older animals’ long transcripts were not as “abundant” as short transcripts, creating “imbalance.”
“Imbalance” likely prohibited the researchers’ discovery of a “specific set of genes” changing.
As animals aged, shorter genes “appeared to become more active” than longer genes.
In humans, the top 5% of genes with the shortest transcripts “included many linked to shorter life spans such as those involved in maintaining the length of telomeres.”
Conversely, the researchers’ review of the leading 5% of genes in humans with the longest transcripts found an association with long lives.
Antiaging drugs—rapamycin (aka, sirolimus) and resveratrol—were linked to an increase in long-gene transcripts.
“The changes in the activity of genes are very, very small, and these small changes involve thousands of genes. We found this change was consistent across different tissues and in different animals. We found it almost everywhere,” Thomas Stoeger, PhD, postdoctoral scholar in the Amaral Lab who led the study, told GEN.
In their paper, the Northwestern scientists noted implications for creation of healthcare interventions.
“We believe that understanding the direction of causality between other age-dependent cellular and transcriptomic changes and length-associated transcriptome imbalance could open novel research directions for antiaging interventions,” they wrote.
While more research is needed to validate its findings, the Northwestern study is compelling as it addresses a new area of transcriptome knowledge. This is another example of researchers cracking open human and animal genomes and gaining new insights into the processes supporting life.
For clinical laboratories and pathologists, diagnostic testing to reverse aging and guide the effectiveness of therapies may one day be possible—kind of like science’s take on the mythical Fountain of Youth.
Proof-of-concept study ‘highlights that using AI to integrate different types of clinically informed data to predict disease outcomes is feasible’ researchers say
Artificial intelligence (AI) and machine learning are—in stepwise fashion—making progress in demonstrating value in the world of pathology diagnostics. But human anatomic pathologists are generally required for a prognosis. Now, in a proof-of-concept study, researchers at Brigham and Women’s Hospital in Boston have developed a method that uses AI models to integrate multiple types of data from disparate sources to accurately predict patient outcomes for 14 different types of cancer.
The process also uncovered “the predictive bases of features used to predict patient risk—a property that could be used to uncover new biomarkers,” according to Genetic Engineering and Biotechnology News (GEN).
Should these research findings become clinically viable, anatomic pathologists may gain powerful new AI tools specifically designed to help them predict what type of outcome a cancer patient can expect.
“Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens,” said Faisal Mahmood, PhD (above) in a Brigham press release. “But that means that this multimodal prediction happens at the level of the expert. We’re trying to address the problem computationally,” he added. Should they be proven clinically-viable through additional studies, these findings could lead to useful tools that help anatomic pathologists and clinical laboratory scientists more accurately predict what type of outcomes cancer patient may experience. (Photo copyright: Harvard.)
AI-based Prognostics in Pathology and Clinical Laboratory Medicine
The team at Brigham constructed their AI model using The Cancer Genome Atlas (TCGA), a publicly available resource which contains data on many types of cancer. They then created a deep learning-based algorithm that examines information from different data sources.
Pathologists traditionally depend on several distinct sources of data, such as pathology images, genomic sequencing, and patient history to diagnose various cancers and help develop prognoses.
For their research, Mahmood and his colleagues trained and validated their AI algorithm on 6,592 H/E (hematoxylin and eosin) whole slide images (WSIs) from 5,720 cancer patients. Molecular profile features, which included mutation status, copy-number variation, and RNA sequencing expression, were also inputted into the model to measure and explain relative risk of cancer death.
The scientists “evaluated the model’s efficacy by feeding it data sets from 14 cancer types as well as patient histology and genomic data. Results demonstrated that the models yielded more accurate patient outcome predictions than those incorporating only single sources of information,” states a Brigham press release.
“This work sets the stage for larger healthcare AI studies that combine data from multiple sources,” said Faisal Mahmood, PhD, Associate Professor, Division of Computational Pathology, Brigham and Women’s Hospital; and Associate Member, Cancer Program, Broad Institute of MIT and Harvard, in the press release. “In a broader sense, our findings emphasize a need for building computational pathology prognostic models with much larger datasets and downstream clinical trials to establish utility.”
Future Prognostics Based on Multiple Data Sources
The Brigham researchers also generated a research tool they dubbed the Pathology-omics Research Platform for Integrative Survival Estimation (PORPOISE). This tool serves as an interactive platform that can yield prognostic markers detected by the algorithm for thousands of patients across various cancer types.
The researchers believe their algorithm reveals another role for AI technology in medical care, but that more research is needed before their model can be implemented clinically. Larger data sets will have to be examined and the researchers plan to use more types of patient information, such as radiology scans, family histories, and electronic medical records in future tests of their AI technology.
“Future work will focus on developing more focused prognostic models by curating larger multimodal datasets for individual disease models, adapting models to large independent multimodal test cohorts, and using multimodal deep learning for predicting response and resistance to treatment,” the Cancer Cell paper states.
“As research advances in sequencing technologies, such as single-cell RNA-seq, mass cytometry, and spatial transcriptomics, these technologies continue to mature and gain clinical penetrance, in combination with whole-slide imaging, and our approach to understanding molecular biology will become increasingly spatially resolved and multimodal,” the researchers concluded.
Anatomic pathologists may find the Brigham and Women’s Hospital research team’s findings intriguing. An AI tool that integrates data from disparate sources, analyzes that information, and provides useful insights, could one day help them provide more accurate cancer prognoses and improve the care of their patients.
International research team that developed swarm learning believe it could ‘significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine’
“Swarm Learning” is a technology that enables cross-site analysis of population health data while maintaining patient privacy protocols to generate improvements in precision medicine. That’s the goal described by an international team of scientists who used this approach to develop artificial intelligence (AI) algorithms that seek out and identify lung disease, blood cancer, and COVID-19 data stored in disparate databases.
Since 80% of patient records feature clinical laboratory test results, there’s no doubt this protected health information (PHI) would be curated by the swarm learning algorithms.
In their study they wrote, “Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. … However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking, and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning.”
What is Swarm Learning?
Swarm Learning is a way to collaborate and share medical research toward a goal of advancing precision medicine, the researchers stated.
The technology blends AI with blockchain-based peer-to-peer networking to create information exchange across a network, the DZNE news release explained. The machine learning algorithms are “trained” to detect data patterns “and recognize the learned patterns in other data as well,” the news release noted.
“Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments,” said Joachim Schultze, MD (above), Director, Systems Medicine at DZNE and Professor, Life and Medical Sciences Institute at the University of Bonn, in the news release. “It’s critical for science to be able to use such data as comprehensively and from as many sources as possible,” he added. This, of course, would include clinical laboratory test results data. (Photo copyright: University of Bonn.)
Since, as Dark Daily has reported many times, clinical laboratory test data comprises as much as 80% of patients’ medical records, such a treasure trove of information will most likely include medical laboratory test data as well as reports on patient diagnoses, demographics, and medical history. Swarm learning incorporating laboratory test results may inform medical researchers in their population health analyses.
“The key is that all participants can learn from each other without the need of sharing confidential information,” said Eng Lim Goh, PhD, Senior Vice President and Chief Technology Officer for AI at Hewlett Packard Enterprise (HPE), which developed base technology for swarm learning, according to the news release.
An HPE blog post notes that “Using swarm learning, the hospital can combine its data with that of hospitals serving different demographics in other regions and then use a private blockchain to learn from a global average, or parameter, of results—without sharing actual patient information.
“Under this model,” the blog continues, “‘each hospital is able to predict, with accuracy and with reduced bias, as though [it has] collected all the patient data globally in one place and learned from it,’ Goh says.”
Swarm Learning Applied in Study
The researchers studied four infectious and non-infectious diseases:
They used 16,400 transcriptomes from 127 clinical studies and assessed 95,000 X-ray images.
Data for transcriptomes were distributed over three to 32 blockchain nodes and across three nodes for X-rays.
The researchers “fed their algorithms with subsets of the respective data set” (such as those coming from people with disease versus healthy individuals), the news release noted.
Findings included:
90% algorithm accuracy in reporting on healthy people versus those diagnosed with diseases for transcriptomes.
76% to 86% algorithm accuracy in reporting of X-ray data.
Methodology worked best for leukemia.
Accuracy also was “very high” for tuberculosis and COVID-19.
X-ray data accuracy rate was lower, researchers said, due to less available data or image quality.
“Our study thus proves that swarm learning can be successfully applied to very different data. In principle, this applies to any type of information for which pattern recognition by means of artificial intelligence is useful. Be it genome data, X-ray images, data from brain imaging, or other complex data,” Schultze said in the DZNE news release.
The scientists say hospitals as well as research institutions may join or form swarms. So, hospital-based medical laboratory leaders and pathology groups may have an opportunity to contribute to swarm learning. According to Schultze, sharing information can go a long way toward “making the wealth of experience in medicine more accessible worldwide.”
Teams from multiple Swedish organizations are investigating the relationship of protein-coding genes to antibodies
Scientists in Sweden are discovering new ways to map the expression of genes in cells, tissues, and organs within the human body thanks to advances in molecular profiling. Their study has successfully combined the analysis of single-cell transcriptomics with spatial antibody-based protein profiling to produce a high-resolution, single-cell mapping of human tissues.
The data links protein-coding genes to antibodies, which could help researchers develop clinical laboratory tests that use specific antibodies to identify and target infectious disease. Might this also lead to a new menu of serology tests that could be used by medical laboratories?
This research is another example of how various databases of genetic and proteomic information—different “omics”—are being combined to produce new understanding of human biology and physiology.
In a Human Protein Atlas (HPA) project press release, Director of the HPA consortium and Professor of Microbiology at Royal Institute of Technology in Stockholm, Mathias Uhlén, PhD, said, “The [Science Advances] paper describes an important addition to the Human Protein Atlas (HPA) which has become one of the world’s most visited biological databases, harboring millions of web pages with information about all the human protein coding genes.”
“We are excited that the new open-access Single Cell Type section constitutes a unique resource for studying the cell type specificity and exact spatial localization of all our proteins”, said Cecilia Lindskog, PhD (above), Head of the HPA Tissue Atlas and Associate Professor, Experimental Pathology, Uppsala University, in the Protein Atlas press release. Medical laboratories may soon have new serology tests to perform that were developed based on HPA data. (Photo copyright: Human Uterus Cell Atlas.)
Distinct Expression Clusters Consistent to Similar Cell Types
To perform their research, the scientists mapped the gene expression profile of all protein-coding genes across different cell types. Their analysis showed that there are distinct expression clusters which are consistent to cell types sharing similar functions within the same organs and between organs of the human body.
The scientists examined data from non-diseased human tissues and organs using three main criteria:
Publicly available raw data from human tissues containing good technical quality with at least 4,000 cells analyzed and at least 20 million read counts by the sequencing for each tissue.
High correlation between pseudo-bulk transcriptomics profile from the scRNA-Seq data and bulk RNA-Seq generated as part of the Human Protein Atlas (HPA).
High correlation between the cluster-specific expression and the expected expression pattern of an extensive selection of marker genes representing well-known tissue- and cell type-specific markers, including both markers from the original publications and additional markers used in pathology diagnostics.
According to the HPA press release, “across all analyzed cell types, almost 14,000 genes showed an elevated expression in particular cell types, out of which approximately 2,000 genes were found to be specific for only one of the cell types.”
The press release also states, “cell types in testis showed the highest numbers of cell type elevated genes, followed by ciliated cells. Interestingly, only 11% of the genes were detected in all analyzed cell types suggesting that the number of essential genes (‘house-keeping’) are surprisingly few.”
Omics-based Biomarkers for Accurate Diagnosis of Disease
The Human Protein Atlas is the largest and most comprehensive database for spatial distribution of proteins in human tissues and cells. It provides a valuable tool for researchers who study and analyze protein localization and expression in human tissues and cells.
Ongoing improvements in gene sequencing technologies are making research of genes more accurate, faster, and more economical. Advances in gene sequencing also could help medical professionals discover more personalized care for patients leading to improved outcomes. A key goal of precision medicine.
One of the conclusions to be drawn from this work is that clinical laboratories and anatomic pathology groups will need to be able to handle immense amounts of data, while at the same time having the capabilities to analyze that data and identify useful patterns that can help diagnose patients earlier and more accurately.
Newly combined digital pathology, artificial intelligence (AI), and omics technologies are providing anatomic pathologists and medical laboratory scientists with powerful diagnostic tools
Add “spatial transcriptomics” to the growing list of “omics” that have the potential to deliver biomarkers which can be used for earlier and more accurate diagnoses of diseases and health conditions. As with other types of omics, spatial transcriptomics might be a new tool for surgical pathologists once further studies support its use in clinical care.
Among this spectrum of omics is spatial transcriptomics, or ST for short.
Spatial Transcriptomics is a groundbreaking and powerful molecular profiling method used to measure all gene activity within a tissue sample. The technology is already leading to discoveries that are helping researchers gain valuable information about neurological diseases and breast cancer.
Marriage of Genetic Imaging and Sequencing
Spatial transcriptomics is a term used to describe a variety of methods designed to assign cell types that have been isolated and identified by messenger RNA (mRNA), to their locations in a histological section. The technology can determine subcellular localization of mRNA molecules and can quantify gene expression within anatomic pathology samples.
In “Spatial: The Next Omics Frontier,” Genetic Engineering and Biotechnology News (GEN) wrote, “Spatial transcriptomics gives a rich, spatial context to gene expression. By marrying imaging and sequencing, spatial transcriptomics can map where particular transcripts exist on the tissue, indicating where particular genes are expressed.”
In an interview with Technology Networks, George Emanuel, PhD, co-founder of life-science genomics company Vizgen, said, “Spatial transcriptomic profiling provides the genomic information of single cells as they are intricately spatially organized within their native tissue environment.
“With techniques such as single-cell sequencing, researchers can learn about cell type composition; however, these techniques isolate individual cells in droplets and do not preserve the tissue structure that is a fundamental component of every biological organism,” he added.
“Direct spatial profiling the cellular composition of the tissue allows you to better understand why certain cell types are observed there and how variations in cell state might be a consequence of the unique microenvironment within the tissue,” he continued. “In this way, spatial transcriptomics allows us to measure the complexity of biological systems along the axes that are most relevant to their function.”
“Although spatial genomics is a nascent field, we are already seeing broad interest among the community and excitement across a range of questions, all the way from plant biology to improving our understanding of the complex interactions of the tumor microenvironment,” George Emanuel, PhD (above), told Technology Networks. Oncologists, anatomic pathologists, and medical laboratory scientists my soon see diagnostics that take advantage of spatial genomics technologies. (Photo copyright: Vizgen.)
According to 10x Genomics, “spatial transcriptomics utilizes spotted arrays of specialized mRNA-capturing probes on the surface of glass slides. Each spot contains capture probes with a spatial barcode unique to that spot.
“When tissue is attached to the slide, the capture probes bind RNA from the adjacent point in the tissue. A reverse transcription reaction, while the tissue is still in place, generates a cDNA [complementary DNA] library that incorporates the spatial barcodes and preserves spatial information.
“Each spot contains approximately 200 million capture probes and all of the probes in an individual spot share a barcode that is specific to that spot.”
“The highly multiplexed transcriptomic readout reveals the complexity that arises from the very large number of genes in the genome, while high spatial resolution captures the exact locations where each transcript is being expressed,” Emanuel told Technology Networks.
Spatial Transcriptomics for Breast Cancer and Neurological Diagnostics
In that paper, the authors wrote “we envision that in the coming years we will see simplification, further standardization, and reduced pricing for the ST protocol leading to extensive ST sequencing of samples of various cancer types.”
Spatial transcriptomics is also being used to research neurological conditions and neurodegenerative diseases. ST has been proven as an effective tool to hunt for marker genes for these conditions as well as help medical professionals study drug therapies for the brain.
“You can actually map out where the target is in the brain, for example, and not only the approximate location inside the organ, but also in what type of cells,” Malte Kühnemund, PhD, Director of Research and Development at 10x Genomics, told Labiotech.eu. “You actually now know what type of cells you are targeting. That’s completely new information for them and it might help them to understand side effects and so on.”
The field of spatial transcriptomics is rapidly moving and changing as it branches out into more areas of healthcare. New discoveries within ST methodologies are making it possible to combine it with other technologies, such as Artificial Intelligence (AI), which could lead to powerful new ways oncologists and anatomic pathologists diagnose disease.
“I think it’s going to be tricky for pathologists to look at that data,” Kühnemund said. “I think this will go hand in hand with the digital pathology revolution where computers are doing the analysis and they spit out an answer. That’s a lot more precise than what any doctor could possibly do.”
Spatial transcriptomics certainly is a new and innovative way to look at tissue biology. However, the technology is still in its early stages and more research is needed to validate its development and results.
Nevertheless, this is an opportunity for companies developing artificial intelligence tools for analyzing digital pathology images to investigate how their AI technologies might be used with spatial transcriptomics to give anatomic pathologists a new and useful diagnostic tool.
