About

Eline Tjetske Luning Prak, MD, PhD, is a Professor of Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania and holds multiple leadership roles within the Perelman School of Medicine at the University of Pennsylvania. She is the Facility Director of the Human Immunology Core and Co-Director of the Immunology Core at the Center for AIDS Research. Her research focuses on studying the antibody repertoire in health and disease, with particular emphasis on B cell development, clonal expansion, and immune responses. Her work involves sequencing DNA rearrangements that create antibodies to better understand B cell maturation, clonal relationships, and immune system dynamics in various tissues and disease states. She aims to develop clinical laboratory tests for identifying and tracking B cell clones, which can inform understanding of immune responses, autoimmunity, and malignancies. Dr. Luning Prak's expertise includes next-generation sequencing-based immune repertoire profiling, clinical immunophenotyping, autoimmune serology, and HLA typing. She is actively involved in research projects that explore B cell clonality in large tissues, B cell subset ontogeny, autoimmunity, and hematologic malignancies, contributing significantly to the field of immunology and translational medicine.

Research topics

• Immunology
• Biology
• Medicine
• Virology
• Pathology
• Genetics
• Molecular biology
• Computational biology
• Biochemistry

Selected publications

• Response to Dr Belviranli Keskin's Comments

  The Journal of Infectious Diseases · 2026-02-02

  articleSenior author
  PublisherDOI

• A Community Challenge to Benchmark Machine Learning

  Figshare · 2026-03-12

  articleOpen access

  Adaptive immune receptors (B- and T-cell receptors; BCRs and TCRs) can recognise a diverse array of different antigens, initiating and maintaining the adaptive immune response. There is immense interest in academia and industry to discover immune receptors for diagnostics and therapeutics utilizing machine learning (ML). We propose the first community challenge to systematically benchmark ML methods on two tasks: given a dataset of immune state-labeled immune repertoires, predict (1) immune state (diagnostic use case), and (2) infer immune-state-associated receptors (therapeutic discovery use case). We conceived a benchmarking database of real-world relevant data including ~75,000 high-fidelity simulated (ground-truth) and experimentally generated TCR repertoires. By quantifying the performance of state-of-the-art techniques and identifying methodological gaps, this community challenge contributes to expediting ML-based solutions for immunodiagnostics and therapeutics discovery.

  PublisherDOI

• A Community Challenge to Benchmark Machine Learning

  Figshare · 2026-03-12

  articleOpen access

  Adaptive immune receptors (B- and T-cell receptors; BCRs and TCRs) can recognise a diverse array of different antigens, initiating and maintaining the adaptive immune response. There is immense interest in academia and industry to discover immune receptors for diagnostics and therapeutics utilizing machine learning (ML). We propose the first community challenge to systematically benchmark ML methods on two tasks: given a dataset of immune state-labeled immune repertoires, predict (1) immune state (diagnostic use case), and (2) infer immune-state-associated receptors (therapeutic discovery use case). We conceived a benchmarking database of real-world relevant data including ~75,000 high-fidelity simulated (ground-truth) and experimentally generated TCR repertoires. By quantifying the performance of state-of-the-art techniques and identifying methodological gaps, this community challenge contributes to expediting ML-based solutions for immunodiagnostics and therapeutics discovery.

  PublisherDOI

• Spatial transcriptomics from pancreas and local draining lymph node tissue reveals a lymphotoxin-β signature in human type 1 diabetes

  Cell Reports · 2026-03-23

  articleOpen access

  This study explores the inflammatory response observed in the pancreas and pancreatic lymph nodes (pLNs) during the natural history of type 1 diabetes (T1D). Using multicell-resolution spatial transcriptomics (ST), we profile individuals without diabetes (ND), at-risk autoantibody-positive (AAb+) individuals, and T1D donors. In the T1D pancreas, we observed global upregulation of inflammation-associated transcripts, including REG family genes, C3, SOD2, and OLFM4. In the T1D pLN, LTB was significantly upregulated within the lymphoid follicles. Using an orthogonal subcellular-resolution ST platform on an independent donor set, we identified follicular B cells as the primary source of LTB in the pLN and observed increased LTB expression in lymphocytes in insulitic lesions proximal to CCL19/CCL21-expressing endothelium. Collectively, these findings highlight lymphotoxin-β and downstream chemokine signatures in the pancreatic lymphatics as well as within the insulitic lesion, which can inform future therapeutic interventions.

  PublisherDOI

• Roads and detours for CAR T cell therapy in autoimmune diseases

  Nature Reviews Drug Discovery · 2026-01-26 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Differential Contributions of IgM and IgG Autoantibodies to Serologic IA2 Reactivity in Type 1 Diabetes

  Biomolecules · 2026-03-26

  articleOpen accessSenior authorCorresponding

  Autoantibodies targeting islet antigen 2 (IA2) are critical diagnostic and prognostic markers for type 1 diabetes (T1D). Standard clinical assays do not differentiate between IgG and IgM isotypes, yet these antibodies have distinct roles in the T1D autoimmunity. We therefore adapted electrochemiluminescence (ECL) assays to separately detect IgG and IgM antibodies against the IA2 intracellular domain (AA601-979). Assay specificity was confirmed by indirect immunofluorescence, which showed autoantibody binding to IA2-overexpressing cells. Plasma samples were analyzed from two independent cohorts: organ donors of the Human Pancreas Analysis Program (HPAP, n = 69) and children from a Janssen–Breakthrough T1D-funded study (n = 65). Diabetics had significantly higher levels of IA2 IgG (p < 0.001) but not IgM (p > 0.05) compared with controls. Notably, IgM and IgG IA2 antibody levels were not correlated. However, IgM modulates IgG detection: IgM depletion increased detected IgG levels to IA2 in some donors, and sera from donors with high IA2-specific IgM levels reduced monoclonal IgG anti-IA2 antibody binding to IA2. Purified IgM from healthy individuals also suppressed monoclonal IgG binding. These findings support distinct, non-redundant roles for IA2-specific IgG and IgM in T1D serology. Isotype-specific autoantibody analysis may improve risk stratification and monitoring of T1D individuals receiving immunomodulatory therapies.

  PublisherOA PDFDOI

• IgM+IgD− B cells in human gut-associated lymphoid tissue have memory features and give rise to IgM+ and IgA+ antibody-secreting cells

  Scientific Reports · 2025-07-22 · 1 citations

  articleOpen access

  B cells in the mucosa have memory features, give rise to class-switched memory B cells and antibody-secreting cells, and likely contribute significantly to the IgA repertoire in human GALT.

  PublisherDOI

• SELECTION OF ANTI-NUCLEAR ANTIGEN (ANA) REACTIVE B CELLS IN SYSTEMIC LUPUS ERYTHEMATOSUS

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-13

  preprint

  ABSTRACT Objective Autoreactive B cells that recognize nuclear antigens are normally present in healthy individuals and patients with systemic lupus erythematosus (SLE), yet their activation and the production of IgG autoantibodies is a hallmark of SLE. The selection process and regulation of these cells in patients with SLE has not been completely understood. To gain insights into tolerance checkpoints and the developmental trajectories of autoreactive clones, we studied the BCR sequences from thousands of anti-nuclear antigen binding (ANA)+ and ANA-B cells from patients with SLE. Methods From a cohort of 13 patients with SLE, we identified and isolated ANA+ and ANA-B cells by flow cytometry using a method based on their binding to nuclear extracts. We sequenced B cell receptor (BCR) heavy chain variable regions and investigated the features of the IgH repertoire of ANA+ and ANA-B cells from naïve, memory and age-associated B cells (ABCs), and from total plasmablasts. Results The frequency of ANA+ B cells was similar in ABCs and naive B cells and higher in both than in memory B cells. We observed preferential usage of some VH (IGHV1-18, IGHV3-21, IGHV3-23|3-23D, IGHV4-34, IGHV4-39 and IGHV4-59) and VJ genes (IGHJ4 and IGHJ6) in B cells from these patients. ANA+ naïve and ANA+ ABCs used different gene segments and have longer CDR3 sequences than ANA+ memory B cells and ANA-subsets. ANA+ ABCs and memory B cells have a lower frequency of somatic hypermutation (SHM) and less activation induced deaminase (AID) targeting to WRC hotspots compared with their ANA-counterparts. Patients with active disease have a lower frequency of SHM in ANA+ ABCs and memory B cells and in ANA-ABCs. Conclusion Compared to memory B cells, ABCs are enriched in autoreactivity. Our results suggest that there is an immune checkpoint that restricts the differentiation of ANA+ naïve B cells into memory B cells and that ANA+ ABCs originate from ANA+ naïve B cells. Lower frequencies of SHM in antigen experienced ANA+ B cells, and particularly ANA+ ABCs, suggest that these cells might be generated through an extrafollicular (EF) pathway, and that in patients with active SLE there is more EF activation.

  PublisherDOI

• B CELL RECEPTOR SEQUENCING REVEALS DISTINCT SELECTION OF AUTOREACTIVE AGE/AUTOIMMUNITY-ASSOCIATED B CELLS IN PATIENTS WITH SLE

  The Journal of Rheumatology · 2025-05-20

  articleOpen access

  PT021 / #495 Topic: AS01 - Adaptive Immunity POSTER TOUR 05: SLE PATHOGENESIS 24-05-2025 10:00 AM - 10:20 AM Background/Purpose Autoreactive B cells that recognize nuclear antigens are normally present in healthy individuals and patients with systemic lupus erythematosus (SLE). Age/autoimmunity-associated B cells (ABCs) are a recently characterized subset of B cells that are reported to be enriched in autoreactivity and differentiate into plasmablasts or plasma cells. The selection process and regulation of autoreactive B cells and ABCs in patients with SLE has not been completely understood. To gain insights into tolerance checkpoints and the developmental trajectories of autoreactive clones, we studied the BCR sequences from thousands of antinuclear antigens (ANA) positive and ANA-negative B cells from patients with SLE. Methods We included 13 patients with SLE. From peripheral blood samples, we identified and sorted ANA+ and ANA- cells from 3 different B cell subsets: naïve, memory, and ABCs, as well as from total plasmablasts. ANA+ cells were identified by flow cytometry using a novel method based on their binding to nuclear extract. We performed bulk B cell receptor sequencing from genomic DNA. We mapped and sequenced B cell receptor (BCR) regions and investigated the features of the immunoglobulin heavy chain (IgH) repertoire of the sorted subsets. Statistical analysis: To compare CDR3 length and SHM at clone level, we used a generalized mixed-effects model design in which patient of origin was included as a random effect and B cell subsets or patient disease activity status as fixed effects. To analyze the patterns of V gene usage of the most prevalent genes across different B cell subsets, we performed Principal Component Analysis (PCA) with scaled and centered data. Results Ten (77%) patients were female. Mean ± SD age of 38.3 ± 11.5 years. According to the PGA score, 8 patients had at least mild activity (PGA ≥ 0.5), and 5 were inactive. ANA reactivity was similar in ABCs (median 8.3%, IQR 4.8-11.9%) and naive B cells (8.3%; 6-9.8%) and higher in both than in memory B cells (4.1%; 3.3-6.3; p<0.05 both comparisons). We observed preferential usage of some VH (IGHV1-18, IGHV3-21, IGHV3-23|3-23D, IGHV4-34, IGHV4-39 and IGHV4-59) and VJ genes (IGHJ4 and IGHJ6) in our cohort. ANA+ naïve and ANA+ ABCs used different gene segments (Figure 1 top panel) and have longer CDR3 regions (Figure 1, lower panel) than ANA+ memory B cells and ANA- subsets, which suggests a close relationship between these 2 subsets. ANA+ ABCs and memory B cells have lower frequency of somatic hypermutation (SHM) compared with their ANA- counterparts (Figure 2, left panel). This suggests extrafollicular (EF) generation of ANA+ antigen experienced B cells. Patients with active disease have a lower frequency of SHM in ANA+ ABCs and memory B cells and ANA- ABCs (Figure 2, right panel), suggesting increased EF activation in patients with active SLE. Figure 1. Figure 2. Conclusions Compared to memory B cells, ABCs are enriched in autoreactivity. ANA+ ABCs have evidence of a different selection process than memory B cells, and are probably directly derived from ANA+ naïve B cells. Our data support that ANA+ B cells, and particularly ANA+ ABCs can contribute to the generation of autoantibodies in patients with SLE through an EF pathway, and that in patients with active SLE there is more EF activation.

  PublisherOA PDFDOI

• B-Cell Subset Representation Predicts SARS-CoV-2 Vaccine Response in Solid Organ Transplant Recipients

  The Journal of Infectious Diseases · 2025-06-03 · 3 citations

  articleOpen accessSenior author

  BACKGROUND: Solid organ transplant recipients (SOTRs) suffer increased morbidity and mortality due, in part, to chronic immunosuppression. The determination of an individual's immune competence is currently difficult but would improve risk assessment and inform medical decisions. We reasoned that correlating qualitative and quantitative measures of the B-cell compartment with serologic responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination would reveal novel B-cell-based predictors of immune competence. METHODS: We performed an integrated analysis of B-cell phenotypes, serology, and antibody repertoires in heart, lung, liver, kidney, and multiorgan transplant recipients and healthcare worker (HCW) controls (62 individuals total). We utilized K-means clustering and correlation analyses to identify B-cell features that correlated with vaccine serology. RESULTS: K-means clustering identified 3 distinct B-cell compartment-based groups in SOTRs, which correlated with serum responses to SARS-CoV-2 vaccination. Group 1 SOTRs had a naive-dominant circulating B-cell pool and serologic responses closest to HCWs. Group 2 SOTRs had reduced naive but hyperexpanded memory B cells (MBCs) and variable vaccine responses that segregated by immunosuppression. Group 3 SOTRs had lymphopenia across B-cell subsets and poor serologic responses. Antibody repertoire analysis showed reduced clonal diversity across SOTRs, regardless of MBC numbers. Even in SOTRs with the largest immune responses, vaccine-specific B cells showed evidence of reduced maturation and clonal diversity. CONCLUSIONS: These findings reveal a hierarchy of B-cell impairment in SOTRs that can be measured rapidly, with implications for immune monitoring and intervention in immunocompromised individuals.

  PublisherOA PDFDOI

Recent grants

• NIH Grant K08CA083977

  NIH · $683k · 2005

• NIH Grant R01AR034156

  NIH · $5.3M · 2014

• NIH Grant R56AI090842

  NIH · $404k · 2012

• NIH Grant R01CA108812

  NIH · $1.2M · 2010

• Radiobiology and Imaging Program

  NIH · $93.0M · 1997–2027

Frequent coauthors

• Wenzhao Meng

  University of Pennsylvania

  70 shared

• E. John Wherry

  University of Pennsylvania

  38 shared

• Kathleen E. Sullivan

  Children's Hospital of Philadelphia

  37 shared

• Jean Boyer

  33 shared

• Kenyetta R. McDonald

  33 shared

• Aaron M. Rosenfeld

  33 shared

• Abbas F. Jawad

  Children's Hospital of Philadelphia

  32 shared

• Carl H. June

  Parker Institute for Cancer Immunotherapy

  25 shared

Labs

• Pathology and Laboratory Medicine, University of PennsylvaniaPI

Education

• AB, Molecular Biology

  Princeton University

• MD

  University of Pennsylvania Perelman School of Medicine

• PhD, Immunology Graduate Group

  University of Pennsylvania