Laura Donlin, PhD

Investigators from HSS and collaborating centers identified distinct immune cell patterns in blood that signal an increased risk of developing rheumatoid arthritis (RA) before symptoms occur. The discovery represents a key milestone in creating a blood test that could help doctors identify patients at higher risk of the disease.

The study, presented today at the annual meeting of the American College of Rheumatology, ACR Convergence 2024, was led by researchers at the University of Colorado School of Medicine in collaboration with investigators from HSS and other centers within the Accelerating Medicines Partnership® Program: Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP® RA/SLE) Network.¹

“These research findings validate the importance of certain immune cells in the development of RA, particularly in how they drive the change from asymptomatic to symptomatic,” said Laura Donlin, PhD, scientist and co-director of the HSS Precision Medicine Program and co-principal investigator of the National Institutes of Health-supported AMP Rheumatoid Arthritis research consortium. “We hope that one day we can use these insights to stop RA before it even begins.”

Rheumatoid arthritis is a type of inflammatory arthritis that can occur when specific types of immune cells attack the body’s healthy tissues, resulting in pain, swelling and stiffness in joints. It can also result in problems in other areas of the body, such as the heart, lungs, eyes, nerves and skin. Diagnosis typically relies on an evaluation by a rheumatologist for clinical signs and symptoms supported by further evidence from X-rays and blood tests.

For the study, the participating rheumatologists collected tissue and blood samples from patients who either had RA or were considered at risk due to specific antibodies, called ACPA, in their blood and having a first-degree relative with the disease. Researchers at the Broad Institute of MIT and Harvard then used advanced single-cell sequencing techniques to analyze the samples and compared the results to those from healthy individuals.

The analysis found higher numbers of certain immune cells in the patients at risk for RA, including CCR2+ T helper cells, T peripheral helper cells, type 1 T helper cells, and granzyme B-positive memory T helper cells. Advanced sequencing also gave the investigators a better understanding of which genes were activated in these cells, offering insight into how the immune cells in patients with RA differ from those in at-risk individuals and healthy people.

“We knew that the presence of ACPA in blood and having a first-degree relative with rheumatoid arthritis indicate risk, but this new research has allowed us to learn more about the underlying biology driving increased risk,” said HSS rheumatologist Susan M. Goodman, MD, a co-author of the study. “These insights will pave the way toward helping us develop a new tool for determining which patients may benefit from early intervention.”

“There is some evidence that existing RA drugs, such as abatacept or rituximab, may help prevent the disease, but they are relatively expensive and come with side effects,” said co-author S. Louis Bridges, Jr., MD, PhD, physician-in-chief and chief of the Division of Rheumatology at HSS. “Pending validation in larger studies, the new patterns of immune cells identified in this research could represent potential targets for the development of new, more effective therapies.”

HSS Authors: S. Louis Bridges, Jr., MD, PhD, Vivian P. Bykerk, BSc, MD, FRCPC, Susan M. Goodman, MD, Laura Donlin, PhD.

Reference:

¹ Inamo J, Keegan J, Griffith A, Ghosh T, Horisberger A, Howard K, Pulford J, Murzin E, Hancock B, Eisenhaure T, Dominguez S, Gurra M, Gurajala S, Jonsson A, Seifert J, Feser M, Norris J, Cao Y, Apruzzese W, Bridges S, Bykerk V, Goodman S, Donlin L, Firestein G, Bathon J, Hughes L, Tabechian D, Filer A, Pitzalis C, Anolik J, Moreland L, Hacohen N, Guthridge J, James J, Cuda C, Perlman H, Brenner M, Raychaudhuri S, Sparks J, Holers M, Deane K, Lederer J, Rao D, Zhang F. Deciphering Pathogenic Phenotypes by Multi-modal Deep Single-cell Blood Immunophenotyping in Individuals At-risk for Rheumatoid Arthritis [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/deciphering-pathogenic-phenotypes-by-multi-modal-deep-single-cell-blood-immunophenotyping-in-individuals-at-risk-for-rheumatoid-arthritis/. Accessed October 10, 2024.

American College of Rheumatology 

A digital pathology approach that can distinguish subtypes of rheumatoid arthritis (RA) using a machine-learning tool created by Hospital for Special Surgery (HSS) and Weill Cornell Medicine (WCM) investigators may help scientists find ways to improve care for this complex condition. 

The study published August 29 in Nature Communications shows that artificial intelligence and machine learning technologies can effectively and efficiently subtype pathology samples from patients with RA.

For this study, Dr. Richard Bell, an Instructor in the HSS Research Institute and Arthritis and Tissue Degeneration Program, and Lionel B. Ivashkiv,  MD, Chief Scientific Officer at HSS teamed up with  Dr. Fei Wang, a professor of population health sciences and the founding director of the Institute of AI for Digital Health (AIDH) in the Department of Population Health Sciences at Weill Cornell Medicine. 

“Our study addresses the analytical bottleneck of pathology research,” Dr. Bell said. “It is very time-consuming and tedious.” “Our tool automates the analysis of pathology slides, which may one day lead to more precise and efficient disease diagnosis and personalized treatment for RA,” said Dr. Wang. “It shows that machine learning can potentially transform pathological assessment of many diseases.”

There are several existing machine learning tools for automatic analysis of pathology slides in oncology. Dr. Wang and his colleagues have been working to expand the use of this technology in other clinical specialties.

Digital Pathology and Precision Medicine in RA

Distinguishing between the three subtypes of RA may help clinicians choose which therapy is most likely to be effective for a particular patient. This personalized medicine approach would represent a breakthrough in the care of patients with RA, and is a major goal of the Research Institute and Division of Rheumatology at HSS, which involves multiple laboratory and clinical investigators. “It’s the first step towards more personalized RA care,” Dr. Bell said. “If you can build an algorithm that identifies a patient’s subtype, you’ll be able to get patients the treatments they need more quickly.”

 The technology may provide new insights into the disease by detecting unexpected tissue changes that humans might miss. By saving pathologists time on subtyping, the tool may also decrease the cost and increase the efficiency of clinical trials testing treatments for patients with different subtypes of RA. “This work represents an important advance in analyzing RA tissues that can be applied for the benefit of patients” Dr. Ivashkiv said.

 Pathologists currently manually classify arthritis subtypes using a rubric to identify cell and tissue characteristics in biopsy samples from human patients—a slow process that adds to the cost of research and may lead to inconsistencies between pathologists.

 The team first trained its algorithm on RA samples from one set of preclinical models, optimizing its ability to distinguish tissue and cell types in the sample and sort them by subtype. They validated the tool on a second set of samples. The tool also yielded new insights into treatment effects in the models, such as reduced cartilage degradation within six weeks of administering commonly used RA treatments.

Then, they deployed the tool on patient biopsy samples from the NIH-supported Accelerating Medicines Partnership Rheumatoid Arthritis research consortium, of which HSS is a major participating site in research led by Laura Donlin, PhD, scientist and co-director of the HSS Precision Medicine Program and rheumatologist Susan  M. Goodman, MD. The new digital pathology approach could effectively and efficiently type human clinical samples. The researchers are now validating the tool with additional patient samples and determining the best way to incorporate this new tool into pathologists’ workflows.

 The team is working to develop similar tools for evaluating osteoarthritis, disc degeneration and tendinopathy.

The research reported in this story was supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medical Sciences, and the National Institute on Aging, all part of the National Institutes of Health, through grant numbers R21AR071670, R01AI175212, UH2AR067690, UC2AR081025, F30AG076326, T32GM007356, AR046713, AR050401, R01AR078268, UC2AR081025, UL1TR001866 and R01AR056702.

HCPLive Rheumatology featuring Laura Donlin, PhD

New York, New York  – A new study published in Nature, conducted by the Accelerating Medicines Partnership (AMP), reports on the identification of new subtypes of rheumatoid arthritis (RA) by analyzing molecular patterns in biopsies from patient joints. These distinctions may improve the efficacy of treatment by allowing doctors to personalize recommendations depending on the subtype of RA a patient has.

Approximately 1.3 million individuals in the U.S. live with rheumatoid arthritis and 18 million worldwide. Considered a systemic autoimmune inflammatory disease, RA primarily affects the joints, causing pain, stiffness and swelling. While treatment strategies have improved in recent years, a process of trial and error is often required to alleviate symptoms and slow the progression of disease. It remains common for patients to switch medications several times over the course of months to years, with some not finding an effective therapy.

The new study, funded by the National Institutes of Health (NIH) and pharmaceutical industry partners, suggests that RA can be characterized into at least four distinct subtypes. Notably, the underlying biology of each corresponds to a different drug target. By comparing the targets in a patient’s joint with the medications to which they respond, doctors may begin to tailor treatments, potentially leading to more rapidly effective treatment outcomes.

"We believe this work lays the foundation for a new era in the treatment of rheumatoid arthritis,” said Laura Donlin, PhD, Co-Senior author and Co-Director of the Derfner Foundation Precision Medicine Laboratory at Hospital for Special Surgery (HSS). “While we have numerous medications available, the chance we'll choose the right one the first time is fairly low, around 40%. It's critical that we get patients the best medication possible as quickly as possible to slow the progression of disease. By understanding the unique features of a patient's condition, we can make more informed decisions and, hopefully, produce more successful  outcomes for patients.”

Dr. Donlin and her team at HSS are hopeful these results will lead to new standards of personalized care for RA.

Their research was made possible by generous support from The Ambrose Monell Foundation, The Carson Family Charitable Trust, and The Tow Foundation.

About HSS Research Enterprise

HSS Research Enterprise stands as the largest musculoskeletal research facility globally. With over 300 dedicated researchers and 20 state-of-the-art laboratories, HSS Research Institute leads in research aimed at enhancing the lives of patients afflicted by debilitating orthopedic and rheumatic conditions, including arthritis, bone and soft tissue injuries, autoimmune diseases, and musculoskeletal pain.

A new study led by Hospital for Special Surgery (HSS) investigators in New York City has found that their computer vision tool effectively distinguishes rheumatoid arthritis (RA) from osteoarthritis (OA) in joint tissue taken from patients who underwent total knee replacement (TKR). The results suggest the machine learning model will help improve research processes in the short term and optimize patient care in the future. The findings were presented today at the European Alliance of Associations for Rheumatology (EULAR) Congress 2022.

TKR is often the only management option for patients with severe knee joint damage. Identifying which disease caused the joint damage is essential for guiding treatment plans, given that RA is a systemic, inflammatory disease that may also affect the eyes or lining around the heart, while OA affects just the joints. “We know there are many more immune cells present in the synovium, or joint tissue, of patients with RA compared to those with OA,” said Bella Mehta, MBBS, MS, rheumatologist at HSS and lead author of the study. “But precisely how many more has not been clear.”

“Pathologists typically assess images of synovium to determine the extent of inflammation using a combination of approaches, including assigning the level of immune cell infiltration on a scale from 0 to 4,” said Dana Orange, MD, MS, rheumatologist at HSS, assistant professor at Rockefeller University and senior author of the study. “However, these methods are imperfect.” For example, a recent study by HSS investigators found that assessments from two highly experienced pathologists evaluating the infiltration of one type of immune cells known as lymphocytes on the same slides agreed only 67 percent of the time.¹

Drs. Orange, Mehta and colleagues at HSS and collaborating institutions developed and validated a computer vision tool that rapidly counts tens of thousands of cell nuclei in whole-slide images of synovium.² For their present study, they measured 14 different pathologist-scored features in synovium from 60 patients with RA and 147 patients with OA who underwent TKR, and used the computer vision tool to determine cell density.

The investigators identified significant differences between RA and OA features in synovium. The RA samples showed increased cell density; low numbers of mast cells, a type of white blood cell; and lower evidence of fibrosis or scarring compared to the OA samples. The probability of correctly distinguishing between RA and OA in synovium was 85 percent when using the 14 pathologist-scored features alone, 88 percent when using the computer’s score for cell density alone and 91 percent when the researchers combined the pathologists’ scores and the computer’s cell density calculation. The researchers determined a cutoff point for distinguishing RA from OA, determining that synovium containing more than 3,400 cells per mm² should be classified as RA.

“While our innovation is not ready for clinical use yet, it holds promise for assisting pathologists in the future,” Dr. Orange said. “Right now, we see it as a valuable tool for research purposes because it provides an accurate and 100% reproducible score of inflammation and look forward to developing it further.”

Dr. Orange added that in the future computer vision could be trained to glean other types of information from tissue samples, including which types of cells are present and whether they are close enough together that they are likely to be communicating with each other. This more granular assessment might enable clinicians to know more precisely which cells are causing tissue damage and tailor treatments accordingly.

Authors: Bella Mehta, MBBS, MS, Susan M. Goodman, MD, Edward F. DiCarlo, MD, Deanna Jannat-Khah, J. Alex Gibbons, Miguel Otero, PhD, Laura Donlin, PhD (HSS), Tania Pannellini, MD, PhD (Weill Cornell Medicine), William Robinson, MD, PhD (Stanford University), Peter K. Sculco, MD, Mark P. Figgie, MD, Jose A. Rodriguez, MD (HSS), Jessica Kirschmann (Stanford University), James Thompson, David Slater, Damon Frezza (The MITRE Corporation), Zhenxing Xu, Fei Wang, PhD (Weill Cornell Medicine), Dana Orange, MD, MS (HSS and Rockefeller University).

References

1. Orange DE, Agius P, DiCarlo EF, et al. Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data. Arthritis Rheumatol. 2018;70(5):690-701. doi:10.1002/art.40428

2. Guan S, Mehta B, Slater D, et al. Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision. ACR Open Rheumatol. 2022;4(4):322-331. doi:10.1002/acr2.11381

A Hospital for Special Surgery (HSS)-led team of investigators is the first to demonstrate that bacterial DNA from prosthetic joint infections can be detected in circulating blood and sequenced to identify the bacteria causing the infection. The innovative approach has the potential to help doctors treat patients who develop prosthetic joint infections with targeted antibiotics faster than is currently possible with standard lab cultures and monitor infection clearance before conducting revision surgeries. The study was published July 22 online first in the Journal of Bone & Joint Surgery.

More than one million Americans undergo knee, hip, elbow and shoulder replacements annually. About one in 75 patients, less than two percent, develop bacterial infections around the joint implants, called prosthetic joint infections. Though the numbers of affected patients are small, the consequences of prosthetic joint infection can be severe.

HSS performs more joint replacement surgeries than any other hospital in the world—a total of 11,000 annually. “Our infection rate is significantly below the national average, but prosthetic joint infections are devastating for affected patients,” says Mathias P. Bostrom, MD, chief of the Adult Reconstruction and Joint Replacement Service at HSS. “Many do not return to the full potential of their original joint replacement.”

It is essential to identify the bacteria causing an infection so that a patient receives the right antibiotic. Since current blood tests only report levels of inflammatory markers, such as C-reactive protein and erythrocyte sedimentation rate, surgeons typically collect a fluid sample from the joint via needle aspiration. However, standard lab test results take at least three days and fail to identify the infectious pathogen in 15 to 20 percent of cases due to the challenges of growing bacteria in culture.

When bacterial identification is unknown, surgeons withhold antibiotics until they obtain tissue samples during the implant removal surgery. The removal procedure involves taking out the contaminated implant hardware and infected soft tissue. Surgeons insert a temporary spacer containing high-dose antibiotics, and they may also place antibiotic beads into the joint. Patients are also treated with high-dose intravenous antibiotics. It takes about six weeks to three months for an infection to clear before patients can undergo another operation to remove the spacer and receive a new permanent joint implant.

The idea for finding a new way to improve the diagnostic approach for patients with prosthetic joint infections began with principal investigator Laura Donlin, PhD, co-director of the Derfner Foundation Precision Medicine Laboratory and a member of the arthritis and tissue degeneration program of the HSS Research Institute. She attended a talk by a professor of bioengineering at Stanford University who had analyzed DNA shed from transplanted organs circulating in blood, called cell-free DNA, as an early detection system for transplant rejection. But through genomic sequencing of circulating cell-free DNA, he had also found increased levels of viral cell-free DNA in some patients’ blood indicating they had developed viral infections while taking immunosuppressant medications.

Dr. Donlin wondered if it would be possible to use the same approach to identify bacteria in patients with prosthetic joint infections. “It was unknown whether bacterial DNA from localized tissue infections around joint implants would be detectable in blood,” says Dr. Donlin. “It’s a challenging environment with many other bits of circulating microbial DNA from skin flora, the gut microbiome and other infections.”

For their proof-of-concept study, Dr. Donlin together with orthopedic surgeons including Michael P. Cross, MD and Dr. Bostrom and colleagues at HSS and Weill Cornell Medicine collected blood samples from 53 patients with known hip or knee prosthetic joint infections beginning in 2018. Karius, a genomic insights company based in Redwood City, California, sequenced blood samples collected from patients before treatment for infection and at the time of reimplantation surgery. They compared microbial cell-free DNA in the blood samples to their proprietary database of more than 1,300 known microbial genomes. The HSS investigators compared sequencing findings with results from standard tissue cultures.

Among surgical tissue samples from these 53 patients, traditional lab cultures identified the bacterial species in 35 cases and the bacterial genus in 11 cases, an overall detection rate of 87 percent. Microbial cell-free DNA sequencing identified the bacterial species in 23 cases in agreement with standard culture results. The new approach also identified the bacterial species in eight cases where cultures identified the bacterial genus only and four cases where cultures failed to determine the presence of bacteria.

On its own, microbial cell-free DNA sequencing pinpointed the bacterial species in 66 percent of samples. However, as an addition to standard culture results, it increased pathogen detection from 87 to 94 percent of samples. Microbial cell-free DNA sequencing was three days faster reporting the bacterial species for cases where culture results had only identified the bacterial family.

Analyses of follow-up blood samples after joint removal surgery and treatment with antibiotics showed undetectable or reduced bacteria levels. “For most patients, we saw microbial cell-free DNA levels drop below detection, indicating the infections had most likely cleared,” says Dr. Donlin. “But there were some cases with lower yet detectable levels after six weeks of antibiotic treatment. Considering that cell-free DNA lives in the bloodstream for only a few minutes, that meant these patients still had an ongoing infection and might require modification to their antibiotic treatment plan.”

“An indication that there is an infection, at least to the genus level for samples that fail to show results in standard cultures, will be beneficial for diagnosing and treating patients earlier for improved outcomes,” says Dr. Bostrom. “As a monitoring tool, microbial cell-free DNA sequencing has the potential to provide important information on the right time to change or stop antibiotics and for reimplantation surgery.”

Dr. Donlin and colleagues are improving the sensitivity and specificity of the new diagnostic method in collaboration with Karius and planning a multicenter study to test it on a larger scale. “In the future, we hope microbial cell-free DNA sequencing will prove to be a useful tool for detecting joint infections more quickly than is currently possible,” Dr. Donlin says. “We’re very excited that our innovation may one day translate to improved outcomes for patients.”

The study was supported by funding provided by the Price Family Foundation, the Stavros Niarchos Foundation Complex Joint Reconstruction Center at HSS, the Feldstein Medical Foundation, the Carson Family Charitable Trust, the Ambrose Monell Foundation, the HSS Research Institute’s David Z. Rosensweig Center for Genomics Research, the National Institutes of Health, the Bill and Melinda Gates Foundation, the Leukemia & Lymphoma Society and the National Science Foundation.

Since doctors began treating cancer patients with immunotherapy drugs called checkpoint inhibitors nearly a decade ago, they have observed that a subset of these patients experience a side effect that clinically looks like inflammatory arthritis. These drugs, which take the natural brake off immune cells called T cells and allow them to attack cancer, can result in T cells also attacking healthy tissues, including the joints.

Now a study from investigators at Hospital for Special Surgery (HSS) and Brigham and Women’s Hospital in Boston has found that the synovial fluid and blood of people experiencing checkpoint inhibitor-induced arthritis is populated by a type of T cells rarely seen in people with other types of inflammatory arthritis. The findings are being presented at the virtual American College of Rheumatology/Association of Rheumatology Professionals annual meeting.

“Although checkpoint inhibitor-induced arthritis looks like other forms of inflammatory arthritis, our findings suggest that they’re not the same,” says study co-author Karmela Kim Chan, MD, a rheumatologist at HSS. “These findings are preliminary, but they are very interesting.”

Checkpoint inhibitor-induced arthritis occurs in about 5% of people taking immunotherapy drugs. The researchers looked at synovial fluid and blood from 10 cancer patients who had experienced this side effect; all of them were being treated with drugs that target the immune checkpoint CTLA-4 and/or PD-1. The team also analyzed blood and synovial fluid from 11 people with rheumatoid arthritis (RA) and nine people with spondyloarthropathies (SpA).

The analysis revealed a unique population of CD38^hiCD127^- CD8 T cells. This designation means that they expressed high levels of a marker called CD38 and did not express a marker called CD127. These T cells were expanded in both the blood and joint fluid of people with checkpoint inhibitor-induced arthritis.

Flow cytometry and RNA sequencing analysis demonstrated these cells to be both cytotoxic and actively proliferating. Furthermore, RNA sequencing suggested that these cells may respond to the immune-related protein interferon, but more research is needed to confirm the significance.

“We don’t yet know if this population of T cells is causing checkpoint inhibitor-induced arthritis or if it’s a common feature of a larger population of people being treated with checkpoint inhibitor drugs,” notes Dr. Chan. “Our next step is study the blood and synovial fluid of more people being treated with these drugs and not limit our analysis only to those who are experiencing these side effects.”

She adds that eventually researchers may be able to develop drugs targeted to the T cells that cause joint inflammation, which could be used to treat this side effect.

Co-authors of this study include HSS rheumatologist Anne R. Bass, MD, and HSS scientist Laura Donlin, PhD.

Medscape featuring Laura Donlin, PhD

Healio Rheumatology featuring Laura Donlin, PhD

Newly identified subsets of cell types present in joint tissue in people with rheumatoid arthritis and how they interact may explain why only some people respond to existing medications, according to two studies by co-senior author Laura Donlin, PhD, Co-Director of the Derfner Foundation Precision Medicine Laboratory at Hospital for Special Surgery (HSS) and collaborating colleagues. The findings suggest exciting new targets for developing precision medicine strategies in the future.

Rheumatoid arthritis (RA) is an autoimmune disease that affects the joints. The immune system mistakenly perceives joint tissue as a harmful invader, like a bacteria or virus, and attacks it, causing inflammation, pain and swelling. RA affects an estimated 1.3 million Americans, about 1% of the population. Critical unmet needs in RA treatment are medications that effectively treat all people with RA, especially those who do not respond to disease-modifying antirheumatic drugs (DMARDs) or biologics.

RA involves a complex interplay between many different types of cells—including T cells, B cells, monocytes and fibroblasts—but the specific subtypes that drive disease progression are largely undefined. Understanding these cell types more precisely may hold valuable information in developing new treatments.

“Right now, the standard approach for treating patients is a trial and error approach. We try the first-line of medication for three months and if it does not work, we try the next one,” says Dr. Donlin. “Sometimes it can take a year or more to find an effective treatment. Meanwhile, the disease progresses to the extent of irreversible damage in some of the cases.”

For the first paper, published in the May 6, 2019 issue of Nature Immunology, co-senior author Dr. Donlin collaborated within the Accelerating Medicines Partnership (AMP) in Rheumatoid Arthritis and Lupus Network (AMP RA/SLE consortium) to create a comprehensive “map” of the cells found in RA joint tissue using advanced sequencing technologies. The AMP RA/SLE consortium is a unique public-private partnership that was created to find new ways to identify and validate promising biological targets for diagnostics and drug development.

The researchers identified 18 unique cell populations in synovial tissue provided by 36 patients with RA. Several of the cell types were present in higher amounts in people with RA compared to control samples from patients with osteoarthritis, a degenerative joint disease that results from deterioration of cartilage due to injury or wear over time. For example, Dr. Donlin and colleagues identified a subset of fibroblasts, cells that make connective tissue, in 15 times greater quantities in RA tissues compared to OA tissues. This fibroblast subset is a major producer of the pro-inflammatory cytokine called interleukin-6 and thereby represents a cell type that may be important to focus on in the development of medications for RA patients.

Dr. Donlin and colleagues were also the first to identify the presence of a subset of autoimmune-associated B cells in synovial tissue. These too were found in large quantities in the RA samples, indicating that this subtype may also be a promising target for future drug development.

“Cutting-edge single-cell RNA sequencing technology allowed us to see the complexity of the cell populations in RA tissue for the first time,” says Dr. Donlin. “However, determining whether these expanded cell populations are a cause or an effect of the disease, will require further research.”

For the second paper, published May 8, 2019 in the journal Science Translational Medicine, co-senior author Dr. Donlin and HSS colleagues conducted additional research using results from the AMP consortium to home in on a particular disease-associating cell type. They discovered an abundant subset of macrophages they referred to as HBEGF⁺ inflammatory macrophages in the RA tissue samples. Macrophages are white blood cells that readily tailor their actions to signals from other cells. In chronically inflamed RA tissue, macrophages are a known source of tumor necrosis factor (TNF), a small protein or cytokine that is involved in inflammatory responses in RA.

Next, the researchers tested how clinically-effective RA medications impacted the HBEGF⁺ inflammatory macrophages and thereby disrupt the disease at the cellular level. They were surprised to discover that COX inhibitors known as nonsteroidal anti-inflammatory drugs (NSAIDs) did significantly alter these macrophages, but they did not stop TNF responses. “This finding may explain why NSAIDs treat pain but are not disease-modifying in RA,” says Dr. Donlin. “A better approach may be to use NSAIDs in combination with anti-TNF medications to shut down both inflammatory pathways.”

An experimental drug developed for cancer treatment, an epidermal growth factor receptor (EGFR) inhibitor called AG-1478, was able to successfully reverse the activity of the HBEGF⁺ inflammatory macrophages in cell studies. “Our experiment demonstrated that it is possible to target activity of these cells, but this drug has significant systemic side effects in people,” says Dr. Donlin. “Our work sets the stage for developing better drugs in the future that could target the same mechanism but in a more specific fashion.”

“Overall, our work to date on these two papers has identified previously unknown subsets of cells and provided new insights about how some of these cell types interact with each other to drive RA,” says Dr. Donlin. “We hope that through a better understanding of the cell populations in individual patients we can provide a means by which we can treat them with precision medicine strategies at the earliest stages of disease.”

Both studies were funded by the National Institutes of Health.

Diagnostics World News reported that researchers from HSS and the New York Genome Center (NYGC) designed a low-cost 3-D printed droplet instrument to study single-cell analysis in patients with rheumatoid arthritis.  

Laura Donlin, PhD, co-director of the Derfner Foundation Precision Medicine Laboratory at HSS and co-author of the study, explained that the tool, miniDrops, provides a low-cost and portable option for researchers.

Dr. Donlin and her team examined rheumatoid arthritis synovial tissue using miniDrops and found that it improved workflow efficiency.  

"The tissue came out of the operating room straight to the bench. [We] put cells from the tissue into the machine within an hour," said Dr. Donlin.

From there, Dr. Donlin noted that they were able to study cells from an unbiased and comprehensive perspective, as well as discover new cell types.

"With this technology, you can begin to figure out among patients how differences in cell compositions relate to treatment responses," she added.

Read the full article at diagnosticsworldnews.com

In collaboration the New York Genome Center (NYGC) and New York University (NYU), HSS researchers used a 3D-printed microfluidic controller to study single cell analysis from patients with rheumatoid arthritis (RA), Science Daily reports.

Single cell analysis could potentially study how individual cells influence disease and respond to treatment, but there is currently a lack of user-friendly and cost-effective tools for researchers.

HSS clinicians used the controller to obtain patient samples on-site and immediately after surgery from RA patients.

Laura Donlin, PhD, co-director of the Derfner Foundation Precision Medicine Laboratory at HSS, said "roughly an hour after surgical excision, individual cells from patient tissues were labeled for single-cell sequencing. From this work, we have classified unrecognized fibroblast subtypes that may prove to be important drug targets for our RA patients."

According to the article, the researchers hope that the instrument will lower the hurdles associated with single cell analysis in basic research and clinical settings.

Read the full article at sciencedaily.com

Additional Coverage:

• Becker's Spine Review: New 3-D printed device enables access to single-cell sequencing, used in HSS study of RA patients: 5 things to know
• Genetic Engineering & Biotechnology News: Novel Instrument for Low-Cost Single-Cell Analysis Developed
• Originally reported on by Front Line Genomics