Funded Research

Alopecia areata (AA) is an autoimmune disease characterized by the immune system mistakenly attacking hair follicles (HFs), resulting in hair loss. This condition often develops suddenly and can lead to considerable emotional and social distress for those affected. Despite advancements in research, the exact reasons behind the immune system's targeting of HFs remain unclear, which hinders the development of new therapies. Under normal conditions, HFs undergo cycles of resting, growing, and regressing phases while maintaining a protective "immune privilege" that helps shield them from immune system attacks. However, in AA, this protective mechanism is disrupted, enabling immune cells to damage the structures of the HFs. This project aims to create detailed maps of HF biology in both healthy and diseased states. To this end, we will use state-of-the-art technologies to identify differences in the HF biology under normal conditions and in the setting of AA. The two techniques that we will use are single-cell RNA-sequencing and spatial transcriptomics. Single-cell RNA-sequencing is a method that profiles thousands of individual cells from skin samples to reveal active genes in each individual cell being analyzed. Spatial transcriptomics, on the other hand, is a technique that localizes gene activity within the tissue itself, showing the positions of immune cells and HF cells in relation to one another. We have developed workflows in our lab that then allow us to combine data from these two techniques, creating a powerful and robust dataset that can be used to identify candidates to be targeted using novel treatment strategies. Our goal is to provide a foundation for more precise treatments that re-establish immune tolerance, support lasting hair regrowth, and improve quality of life for individuals with AA. Our research has three aims. First, we will create a normal HF atlas by defining cellular and molecular features of healthy follicles across resting, growing, and regressing stages. Next, we will create a disease progression atlas by tracking immune infiltration and HF disruption during different stages of AA in a mouse model. Finally, we will analyze skin samples from individuals with AA and healthy volunteers. These data will then allow us to integrate findings with the mouse data to identify shared disease mechanisms. This project aims to provide the alopecia community with the most comprehensive dataset on HF biology, both in healthy and during autoimmune attacks. It will clarify the sequence of events that lead from immune infiltration to HF damage and will highlight specific interactions between immune cells and different compartments of the HF. These findings will help researchers prioritize therapeutic targets that focus on restoring protection around the HF and preventing relapse. By the conclusion of this study, we expect to deliver (i) a complete cellular and spatial reference map of the normal hair cycle, (ii) a dynamic timeline of immune engagement during AA, and (iii) a cross-species framework linking mouse and human data. These data will identify potential therapeutic candidates that can be assessed in mouse models that would have a high likelihood of relevance in human AA.

Alopecia areata (AA) is an autoimmune disease that causes sudden, often relapsing hair loss. It affects up to 2% of people worldwide and can progress to total baldness, with major effects on quality of life. Current treatments are limited, and we do not fully understand why the immune system targets hair follicles or why the disease waxes and wanes. This project will focus on epigenetics—the chemical marks and structural features that control how genes are turned on and off in each cell type. Unlike genetic variants, which are fixed within individuals, epigenetic changes differ between cell types and can shift over time, making them especially important for diseases like AA that involve both immune cells and hair follicle cells and that change course between relapse and remission. A key epigenetic mark is DNA methylation, which is both mechanistically important and clinically promising. In type 1 diabetes, for example, methylation differences in immune cells can be detected even before diagnosis, highlighting its value as a potential biomarker. At the same time, DNA methylation is technically challenging to measure, especially at single-cell resolution. Using new single-cell technologies, we will measure DNA methylation in thousands of individual cells from scalp biopsies, capturing both the immune cells that attack and the follicle cells that are damaged. By combining this with single-cell gene expression data, we will build the first integrated map of how epigenetic programs change in both compartments of AA. We will also investigate whether transposable elements (TEs) - ancient, virus-derived, repetitive DNA sequences that make up over half of our genome – play a role in AA. In healthy tissues TEs are kept silent by DNA methylation, but can contribute to disease when reactivated, by triggering immune responses, such as in systemic lupus erythematosus, or by altering expression of proximal genes, such as in colorectal cancer. Exploring whether changes in DNA methylation at TEs contribute to AA will open novel biological and therapeutic directions for this disease. The results of this study will improve our understanding of what drives AA, establish DNA methylation as a window into disease mechanisms and biomarkers, and lay the groundwork for better classification of disease subtypes and future targeted treatments.

Why this study matters: Alopecia areata (AA) is an autoimmune disease that causes hair to fall out in round patches. When AA happens in children, it can be especially hard— kids may feel anxious, lose confidence, or avoid school and activities. Doctors now have better treatments, but it is still difficult to predict who will respond and when new hair growth will start. Scalp biopsies can provide information, but they are procedures we try to avoid in children. What we will do: This project will test a painless, child-friendly way to “read” the immune activity in the hair follicle by gently plucking a few hairs and studying the genes that are turned on or off in those bulbs. We will enroll children ages 6–17 with patchy AA who are starting medically appropriate treatment (e.g., JAK inhibitors, methotrexate, dupilumab, systemic corticosteroids), and a comparison group of healthy children. Each participant will have a small set of hairs plucked at the start of treatment, at 12 weeks, and at 24 weeks. We will look for gene-activity patterns—especially those linked to the immune pathways known to drive AA—and see how these patterns change with treatment. We will also track hair regrowth with standardized physician-assessed scales alongside patient-reported outcomes. Why hair plucks: Hair plucks are quick, safe, and do not require numbing or stitches. They let us collect the cells that matter most in AA (e.g., the hair-bulb and surrounding outer root sheath) so we can measure changes in immune signals over time without performing a biopsy. What we expect to learn: We aim to learn two things: (1) what the immune activity looks like at baseline in children with AA compared with healthy peers, and (2) whether early changes in those immune signals at 12 weeks predict visible hair regrowth by 24 weeks. We will also scan broadly for new genes and pathways that could explain why some children respond to treatment and others do not. How this will help families: If successful, this study will deliver the first noninvasive “molecular check-in” for pediatric AA—helping clinicians and families know sooner whether a treatment is working. It will also lay the groundwork for larger studies and future precision medicine tools that personalize care, reduce unnecessary procedures, and improve the experience of both children and adults living with AA.

Alopecia areata (AA) is an autoimmune disease that causes hair loss, affecting millions of people worldwide. It can appear as small, round patches of hair loss or, in severe cases, widespread thinning across the scalp or body. While AA is not life-threatening, it can deeply affect emotional well-being, often leading to anxiety, depression, and low self-esteem. Diagnosis is often challenging because AA shares features with other types of hair loss, which can delay treatment and increase stress for patients. Our project uses advanced tools-3D tissue imaging, machine learning, and genetic mapping-to improve how AA is diagnosed and understood. Traditionally, doctors look at thin, two-dimensional slices of skin under the microscope, which can miss important details since hair follicles are three-dimensional structures. We have developed computer algorithms that "stack" these thin slices to create a 3D model of the hair follicle and surrounding tissue, giving a much clearer view of disease features. In early work, we have already created 3D tissue maps from 12 patient samples, including 3 with AA and 9 with other types of hair loss for comparison. These pilot studies show that our computer model can accurately recognize key features of hair disease, providing proof that this approach can be used to distinguish AA from its mimickers. We also plan to use a technique called spatial transcriptomics, which allows us to see which genes are active in precise regions of the skin. By combining this genetic information with our 3D maps, we can better understand how the immune system attacks hair follicles in AA. Importantly, this may also answer a critical clinical question: why do some patients respond to JAK inhibitor medications, while others do not? By comparing samples from six patients who improved with JAK inhibitors and six who did not, we aim to identify genetic or tissue-level differences that could guide future personalized treatments. In summary, this project brings together cutting-edge technology and patient-focused research to transform how alopecia areata is diagnosed, studied, and treated. By improving accuracy and uncovering reasons for treatment resistance, our work has the potential to directly improve care for people living with this condition.

The SPARC AA project maps hair loss in patients with alopecia areata and them uses customized computer programs to analyze changes over time. The changes will be correlated with age, severity of alopecia, treatments and exposures to viruses, vaccines and the environment. Why we are studying “where” hair loss happens? Alopecia areata causes patches of hair loss, but usually, we only measure how much hair is lost, not where it happens. We think the pattern of hair loss on the scalp could provide important clues. For example, if patches start at the front or spread evenly across the scalp, this might tell us something about the course of the condition compared with patches that stay small and clumped together. What this study will do? We will use photos of the scalp and mark them on a simple “mapped grid,” using computer programming. We will then follow the hair loss pattern over time and correlate this with other patient factors and exposures. By learning more about “where” alopecia areata happens and how this changes over time, we hope to understand and record disease progression more closely. What impact will this study have? This is a smaller study to validate the artificial intelligence platform and create the infrastructure needed to extend this process to patients and to clinical trials.

Over the past two years, our multidisciplinary team including clinicians and biomedical engineers has focused on the development of revolutionary microneedle platform for the management of AA. In this proposal, we aim to rectify the local Treg deficiency in AA patients, by recruiting and amplifying Treg function, locally at the affected skin lesion, using our novel hydrogel-based microneedle (MN) platform for chemokine/cytokine delivery. Our proposal is built on the foundation of our work published recently (Artzi, Azzi, and colleagues, Advanced Functional Materials, 2021) 12, where we demonstrated that our engineered polymeric microneedle platform supports local delivery of CCL22 and IL-2 to promote Treg homing and expansion for prevention of allograft rejection in murine skin allograft model. The skin offers unique clinical opportunity for direct and easy access to an intricate network of immune cells including Tregs, which we leveraged for targeted transcutaneous-delivery of immunotherapies using MNs. Our MN-platform fabricated with hyaluronic acid (HA), allowed transdermal delivery of immunomodulators for local action in the skin allografts, while obviating systemic side effects in peripheral organs such as the spleen—a commonly observed phenomenon following systemic delivery of immune-suppressive agents. Moreover, our MNs allow sampling of interstitial skin fluid (ISF), a unique biofluid offering diagnostic insights into the perfusion of analytes. The matrix of our MN platform has been engineered to contain disulfide bonds, enabling us to dissolve the needles following retri eval and isolate cellular biomarkers which can be used to report on the disease state. Based on the foundation of this work and its relevance to restoring Treg homeostasis, we have used our MN platform to deliver chemokines and cytokines locally at the AA lesion site to recruit and restore Treg number and function. In an AA murine model, we have demonstrated that our MNs delivering CCL22 (for Treg recruitment), and IL-2 (for Treg proliferation) remodeled the immune profile at the hair follicle level, increasing Treg frequencies and reversing AA symptoms as targeted, sustained hair regrowth was observed in the AA lesions (manuscript submitted). We also demonstrated that the T cell immune profile in ISF when extracted with the MNs correlate with that in the skin. Here, we aim to expand our understanding on the cellular mechanism driving disease recovery and the potential of Treg-centric therapies, as opposed to immunosuppressive agents, for the management of AA. We predict that our technology solution will be able to provide efficacy and durability of the therapeutic effects, due to the unique mechanism of action of our agent combination that addresses the root cause of AA pathogenesis at the local lesion level, and will harbor the required safety profile by virtue of the minimally invasive, painless topical application of our novel polymeric MN-based platform to deliver nanogram level drug doses, eliminating systemic side effects and local pain. By establishing an optimal balance of the benefit/risk profile, a crucial piece missing from the current treatment strategies, in addition to enhancing the quality of life and compliance for the patient, our technology has the potential to be not only a strong contender, but a game -changer in the AA treatment landscape.

Alopecia Areata (AA) is one of the most common autoimmune diseases in the US with a lifetime risk of 1.7%. AA causes significant disfigurement and psychological distress to AAaffected individuals. The etiology of AA is still incompletely understood. The T -cell-mediated autoimmune hypothesis with the collapse of hair follicle (HF) immune privilege is most likely and develops under a genetic background [3]. We recently identified CD8+NKG2D+ T cells as the key pathogenic cells in AA [4]. The role of CD8+NKG2D+ T cells in the process of HF destruction has remained enigmatic. We previously used next-generation sequencing of the TCRβ repertoire in the C3H/HeJ mouse model of AA. We observed interindividual sharing of TCRβ chain protein sequences, which strongly supports a model of antigenic drive in AA. However, the relationship between CD8+ T cell clonality and pathogenicity is poorly understood. We leveraged recent advances in single-cell technology and performed parallel single-cell RNA and T cell receptor (TCR) sequencing in the graft-induced C3H/HeJ mouse model of AA to interrogate time-dependent changes in the AA immune landscape with respect to both gene expression profiles and T cell clonality. Using the highly expanded CD8+ TCR sequences identified in our study, we performed TCR retrogenic mice and TCR engineered T cells to demonstrate that expanded CD8+ T cell clones are sufficient and necessary for AA, establishing the causal relationship between CD8+ T cell clonality and pathogenicity in disease. However, in AA, the antigen epitopes recognized by alopecic T cells has remained unknown, mainly due to the lack of genome-scale and high-throughput antigen discovery tools. Previous studies showed that using online matrix-assisted algorithms could predict MHC binding peptide sequences for autoimmune diseases. One limitation of this method is that only a small number of antigens can be discovered. Cell-based screening methods for CD8+ T cells, such as T-Scan, have achieved proteome-wide success in identifying cognate antigens for TCRs from CD8+ cells. T-Scan utilizes the granzyme-mediated cytolytic ability of CD8+ cells toward target cells that express a library of candidate antigens that are processed and presented endogenously on MHC-I molecules as a functional readout. Given the power of T-Scan in finding T cell epitopes, we postulate that T-Scan would facilitate the discovery of HF auto-antigens recognized by alopecic CD8+ T cells. Successful identification of antigen epitopes in the AA mouse model will provide knowledge to find antigen epitopes in human AA and will enable the development of specific therapeutic modalities targeting antigen - specific T cells.