Biology subjects

James, J. A.

Publications and source records attributed to James, J. A..

16 recordsLinked to original sources

bioRxiv2026

Rethinking the Benign Nature of Class II Lupus Nephritis

Introduction: Class II lupus nephritis (LN) is considered clinically benign, yet up to 50% of patients progress to more severe disease and no molecular characterization exists. We aimed to define the single-cell landscape of Class II LN and identify cellular and transcriptional features associated with kidney outcome. Methods: We performed single-cell RNA sequencing on kidney biopsies from fifteen Class II LN patients (eight de novo, seven regressed from prior proliferative or membranous disease) and six healthy controls, integrated with 155 LN and 30 healthy-control samples from the Accelerating Medicines Partnership in SLE spanning proliferative, membranous, and mixed LN. Findings were correlated with 52-week renal response and supported by urinary proteomics. Results: Despite histologically minimal changes, Class II LN exhibited marked immune cell expansion comparable to proliferative and membranous LN, including CCL3+ CCL4+ intermediate monocytes producing TNF, FOLR2+ SIGLEC1+ macrophages producing TGF{beta} and PDGF, and CD69+ CD8+ effector memory T cells producing IFN{gamma}. Fibroblasts and myofibroblasts were significantly expanded and displayed divergent transcriptional programs: pro-fibrotic and inflammatory myofibroblast modules were associated with worse 52-week outcomes, while interferon-responsive and matrix-remodeling fibroblast programs were associated with improvement. Pathogenic and protective fibroblast populations received overlapping immune-derived signals, suggesting that fibroblast transcriptional state, rather than the identity of incoming signals, shapes the stromal response. Baseline chronicity index captured this stromal heterogeneity and was the clinical parameter most associated with outcome. Urinary proteomics aligned Class II with proliferative rather than membranous disease. Conclusions: Class II LN harbors substantial molecular activity not captured by routine histology. Fibroblast transcriptional programs and baseline chronicity index are candidate correlates of kidney outcome that warrant validation in larger, prospectively followed cohorts.

genomics↗

bioRxiv2026

Serial Immunohistochemistry for High-Dimensional Single-Cell Spatial Analysis of Human Kidney Biopsies

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

pathology↗

bioRxiv2026

ARID3a-Expressing Naive B Cells in SLE have an Activated Phenotype and Transiently Express Surface CD68

Numbers of ARID3a (AT-Rich Interaction Domain 3a) -expressing B lymphocytes from patients with systemic lupus erythematosus (SLE) are associated with increased disease activity. Normally, ARID3a-expressing circulating naive B cells are rare, but in SLE naive B cells dramatically increase ARID3a expression. We found that in vitro stimulation of B lymphocytes from healthy individuals with a cocktail of cytokines and agonists induced ARID3a in a subset of activated naive B cells and in IgD-CD27- double negative B cells previously associated with autoimmunity. Single cell RNA-seq of isolated naive B cells from ten SLE patients, with varying frequencies of ARID3a-expressing cells, revealed that ARID3a-associated genes included activation markers. Moreover, our data revealed the unexpected co-expression of the scavenger receptor CD68 with ARID3a, at both the transcript and protein level, in activated subsets of naive B cells. Inhibition of ARID3a in stimulated B cell cultures blocked naive B cell activation and CD68 expression. Together, these data identify ARID3a and CD68 as markers of naive B cell precursors associated with autoimmunity in SLE.

immunology↗

bioRxiv2026

Serum proteomic atlas reveals distinct molecular signatures of lupus nephritis activity, chronicity, and treatment response

Lupus nephritis (LN), a severe manifestation of systemic lupus erythematosus (SLE), features heterogeneous renal pathology and reliance on invasive biopsies for diagnosis, prognosis, and treatment selection. Current peripheral clinical markers inadequately capture disease activity and progression. Here, we performed comprehensive serum proteomic profiling of over 5,000 proteins in the large, longitudinal Accelerating Medicines Partnership Rheumatoid Arthritis/SLE cohort of 270 LN patients and 63 healthy controls. Machine learning identified distinct molecular signatures that classified LN versus controls, differentiated histological classes, and delineated activity- and chronicity-associated pathways, including inflammatory cytokine, PI3K/AKT, TGFb, and complement/coagulation pathways. An increase in VSIG4, CD27, HAVCR1, and LAIR1 consistently emerged as top biomarkers across multiple clinical contexts, and early decreases in these markers at 3 months were associated with complete treatment response at 1 year. By resolving coordinated serum protein modules linked to key inflammatory, PI3K/AKT, TGFb, and complement pathways, these signatures mechanistically connect circulating proteomic perturbations to intrarenal immune activation, tissue injury, and repair in LN. These findings demonstrate that serum proteomics reflect complex intrarenal immunopathology and offer a promising noninvasive "liquid biopsy" approach to refine LN classification and guide personalized management, potentially reducing the need for repeated invasive biopsies and improving therapeutic decision-making.

immunology↗

bioRxiv2025

Deep profiling of lupus nephritis kidneys reveals dynamic changes in myeloid cells associated with disease progression

ObjectivesLupus nephritis (LN) is a common, potentially fatal manifestation of systemic lupus erythematosus. We aim to gain new insights into the immune responses underlying LN and their relation to the histologic heterogeneity observed in this disease, focusing on myeloid cells. MethodsSingle-cell RNA-sequencing (scRNA-seq) was used to profile dissociated kidney samples from 156 LN patients and 30 healthy individuals. Spatial transcriptomics (ST), utilizing a gene panel designed to capture all myeloid subsets identified in the scRNA-seq data, was applied to kidney samples acquired from 6 LN patients and 2 healthy controls. ResultsWe generated a full catalog of the myeloid subsets found in LN kidneys. Our analyses indicated that an increase in irreversible tissue damage, as measured by the NIH chronicity index (CI), is associated with a gradual switch of the local immune response from one dominated by monocytes and macrophages to one featuring expanded CD4+ T, GZMK+CD8+ T, B and dendritic cells, with a parallel decrease in the interferon response. In proliferative/mixed LN only, the degree of active inflammation correlates with expansion of disease-specific macrophage (DMac) subsets, which later contract as the CI increases. Trajectory analysis of the scRNA-seq data suggested that DMacs arise from both infiltrating monocytes and tissue-resident macrophages; this was supported by the ST data, as well as cell cultures. DMacs are indicated to interact with parietal epithelial cells, promoting the development of glomerulosclerosis. ConclusionsWe suggest a detailed picture of the changes in the kidney immune mechanisms in LN as this disease progresses.

immunology↗

bioRxiv2025

Persistent CD4+ T cell functional deficits during recovery from prolonged symptomatic SARS-CoV-2 infection

Symptoms of acute SARS-CoV-2 infection often resolve quickly but are sometimes associated with persistent immune dysfunction. The factors that predispose individuals to compromised immune function have not been well defined. We investigated CD4+ T cell phenotype and function in a small cohort of individuals who recovered from mild to moderate SARS-CoV-2 infection without hospitalization and were divided into short or prolonged symptom duration groups. Five individuals with prolonged symptom duration showed marked downregulation of CD4 on CD3+CD8- T cells (CD4low group) and a poor response to TCR stimulation with the superantigen Staphylococcal enterotoxin B (SEB), as shown by weak upregulation of the activation markers CD134 and CD69. CD4 surface intensities recovered to normal levels in four of these individuals within 3-12 months. Selected cytokines (IL-1RA, IL-7, and VEGF) were elevated in individuals with low CD4, but plasma levels of anti-S1 IgG did not correlate with CD4 defects. Bulk RNA sequencing of unstimulated and SEB-treated CD3+CD8- T cells revealed a >50% reduction in the number of differentially expressed genes in the CD4low group compared to the same individuals after CD4 levels were recovered and a healthy control group. Upstream regulator analysis of differentially expressed genes in unstimulated CD4low cells suggested a response to IFN, while SEB-stimulated CD4low cells showed reduced functionality of IL-2, CD28, and SATB1 regulated pathways. In summary, prolonged symptomatic recovery from SARS-CoV-2 infection was associated with a global CD4+ T cell response defect, defined by low surface CD4 expression, evidence of IFN signaling, and defective T cell activation.

immunology↗

bioRxiv2025

A population-scale atlas of blood and tissue in lupus nephritis

One Sentence SummaryA single-cell atlas of paired blood and tissue samples from Lupus Nephritis patients and healthy controls identified stromal and immune populations within renal tissue, including the scar-associated macrophage populations, which correlate with and may drive renal disease activity. Lupus nephritis (LN), a severe manifestation of Systemic Lupus Erythematosus (SLE), is a heterogeneous disease driven by diverse immune and tissue cell types. We obtained 538K single-cell and 140K single-nuclear profiles from kidney biopsies of 155 LN patients and 30 pre-implantation transplant biopsy controls, along with 325K single-cell blood profiles overlapping many of these patients. We identified key tissue cell types and cell states, and immune cell states; we were able to determine cell states that were tissue specific, and those that were present in the blood. We observed that LN pathological features are significantly associated with cell states using differential gene expression and Covarying Neighborhood Analysis (CNA). These analyses revealed broad changes in cell states associated with irreversible chronic tissue damage. After controlling for the effects of ongoing tissue damage, we observed that expansion of key glomerular and Scar Associated Macrophages (SAMs) populations tracked with increasing inflammatory disease activity. SAMs appear to drive LN fibrosis and, in active disease, infiltrate the glomeruli more than other myeloid cells. These observations strongly support that therapeutic targeting of myeloid populations may offer an as-of-yet unproven strategy to prevent renal inflammation and ongoing kidney damage in LN.

molecular biology↗

bioRxiv2023

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in At-Risk populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5low naive B cell population was expanded in ACPA-positive FDRs. Further, we developed an "RA immunophenotype score" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets.

genomics↗

bioRxiv2023

Intrarenal myeloid subsets associated with kidney injury are comparable in mice and patients with lupus nephritis

Resident macrophages and infiltrating monocytes in kidneys of patients with lupus nephritis are altered both in frequency and function relative to their counterparts in healthy kidneys. The extent to which mouse models might be useful in developing approaches to target these cells for treating lupus nephritis is poorly understood. Here, we studied four common lupus mouse models that share clinical, serologic, and histopathologic kidney changes with humans. Using single-cell profiling and multiplex spatial imaging to analyze the intrarenal myeloid compartment with the onset of clinical disease in these models, we identified monocyte and macrophage subsets that expand or contract in kidneys with clinical nephritis. A unique subset of classical monocytes expanded with the onset of disease and expressed genes such as CD9, Spp1, Ctsd, Cd63, Apoe, and Trem2 that were previously shown to be induced by tissue injury and play a role in inflammation, lipid metabolism and tissue repair in other organs. Resident macrophages transitioned from a pro-inflammatory to a similar injury-associated state with onset of disease. To test whether these findings in mouse models were also observed in humans, we re-analyzed monocytes and macrophages in a single-cell RNAseq dataset of kidney biopsies from 155 patients with lupus nephritis and 30 healthy donors, collected by the NIH AMP RA/SLE consortium. Human monocytes and macrophages showed conserved changes in gene expression programs associated with lupus nephritis disease indices, and localized to similar kidney microenvironments as in mice. By identifying myeloid subsets and disease-associated alterations in biological processes that are conserved across species, we provide a strong rationale for functional studies of these cells and pathways in mice to uncover mechanisms and find targets relevant to human lupus nephritis. One sentence summaryThis study characterizes intrarenal myeloid cells from four lupus mouse models and 155 patients with lupus nephritis using single-cell RNA-seq and imaging, and identifies novel infiltrating and resident myeloid subsets that are conserved between mouse and human lupus nephritis, thus providing a map and strong rationale for functional studies in mice with relevance to human disease.

immunology↗

bioRxiv2023

Peptidoglycan from Bacillus anthracis Inhibits Human Macrophage Efferocytosis in Part by Reducing Cell Surface Expression of MERTK and TIM-3

Bacillus anthracis peptidoglycan (PGN) is a major component of the bacterial cell wall and a key pathogen-associated molecular pattern (PAMP) contributing to anthrax pathology, including organ dysfunction and coagulopathy. Increases in apoptotic lymphocytes are a late-stage feature of anthrax and sepsis, suggesting there is a defect in apoptotic clearance. Here, we tested the hypothesis that B. anthracis PGN inhibits the capacity of human monocyte-derived macrophages (M{Phi}) to efferocytose apoptotic cells. Exposure of CD163+CD206+ M{Phi} to PGN for 24h impaired efferocytosis in a manner dependent on human serum opsonins but independent of complement component C3. PGN treatment reduced cell surface expression of the pro-efferocytic signaling receptors MERTK, TYRO3, AXL, integrin V{beta}5, CD36 and TIM-3, whereas TIM-1, V{beta}3, CD300b, CD300f, STABILIN-1 and STABILIN-2 were unaffected. ADAM17 is a major membrane-bound protease implicated in mediating efferocytotic receptor cleavage. We found multiple ADAM17-mediated substrates increased in PGN-treated supernatant suggesting involvement of membrane-bound proteases. ADAM17 inhibitors TAPI-0 and Marimastat prevented TNF release, indicating effective protease inhibition, and modestly increased cell-surface levels of MerTK and TIM-3 but only partially restored efferocytic capacity by PGN-treated M{Phi}. We conclude that human serum factors are required for optimal recognition of PGN by human M{Phi} and that B. anthracis PGN inhibits efferocytosis in part by reducing cell surface expression of MERTK and TIM-3.

immunology↗

bioRxiv2022

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

bioRxiv2022

Genetic load in incomplete lupus erythematosus

Incomplete lupus erythematosus (ILE) patients have lupus features but insufficient criteria for systemic lupus erythematosus (SLE) classification. Some ILE patients transition to SLE, but most avoid major organ involvement. This study tested whether the milder disease course in ILE is influenced by reduced SLE-risk allele genetic load. We calculated the genetic load based on 99 SLE-associated risk alleles in European American SLE patients (>4 ACR-1997 criteria, n=171), ILE patients (3 ACR-1997 criteria; n=174), a subset of ILE patients not meeting SLICC classification (ILESLICC, n=119), and healthy controls (n=133). ILE and SLE patients had significantly greater SLE-risk allele genetic load compared to healthy controls, while ILESLICC patients had a trend toward an increased genetic load, although not statistically significant. However, the genetic load did not differ between ILE and SLE. In conclusion, ILE and SLE patients have comparable genetic loads of SLE risk loci, suggesting similar genetic predisposition between these conditions. Phenotypic differences between SLE and ILE may instead be influenced by ILE-specific variants and gene-environment interactions.

immunology↗

bioRxiv2021

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial Intelligence (AI) can extract clinically actionable information from medical image data. In cancer histopathology, AI can be used to predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets whose collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL) where partners jointly train AI models, while avoiding data transfer and monopolistic data governance. Here, for the first time, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images comprising over 5000 patients. We show that AI models trained using Swarm Learning can predict BRAF mutational status and microsatellite instability (MSI) directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer (CRC). We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States of America and validated the prediction performance in two independent datasets from the United Kingdom using SL-based AI models. Our data show that SL enables us to train AI models which outperform most locally trained models and perform on par with models which are centrally trained on the merged datasets. In addition, we show that SL-based AI models are data efficient and maintain a robust performance even if only subsets of local datasets are used for training. In the future, SL can be used to train distributed AI models for any histopathology image analysis tasks, overcoming the need for data transfer and without requiring institutions to give up control of the final AI model.

bioinformatics↗

bioRxiv2021

HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

cancer biology↗

bioRxiv2017

Bcl-xL as a poor prognostic biomarker and predictor of response to adjuvant chemotherapy specifically in BRAF-mutant stage II and III colon cancer

BackgroundBRAF mutation occurs in 8-15% of colon cancers (CC), and is associated with poor prognosis in metastatic disease. Compared to wild-type BRAF (BRAFWT) disease, stage II/III CC patients with BRAF mutant (BRAFMT) tumors have shorter overall survival after relapse; however, time-to-relapse is not significantly different. The aim of this investigation was to identify, and validate, novel predictors of relapse of stage II/III BRAFMT CC.\n\nPatients and methodsWe used gene expression data from a cohort of 460 patients (GSE39582) to perform a supervised classification analysis based on risk-of-relapse within BRAFMT stage II/III CC, to identify transcriptomic biomarkers associated with prognosis within this genotype. These findings were validated using immunohistochemistry in an independent population-based cohort of Stage II/III CC (n=691), applying Cox proportional hazards analysis to determine associations with survival.\n\nResultsHigh gene expression levels of Bcl-xL, a key regulator of apoptosis, were associated with increased risk of relapse, specifically in BRAFMT tumors (HR=8.3, 95% CI 1.7-41.7), but not KRASMT/BRAFWT or KRASWT/BRAFWT tumors. High Bcl-xL protein expression in BRAFMT, untreated, stage II/III CC was confirmed to be associated with an increased risk of death in an independent cohort (HR=12.13, 95% CI 2.49-59.13). Additionally, BRAFMT tumors with high levels of Bcl-xL protein expression appeared to benefit from adjuvant chemotherapy (P for interaction =0.006), indicating the potential predictive value of Bcl-xL expression in this setting.\n\nConclusionsThese findings provide evidence that Bcl-xL gene and/or protein expression identifies a poor prognostic subgroup of BRAFMT stage II/III CC patients, who may benefit from adjuvant chemotherapy.\n\nKey MessageO_LIUsing a combination of computational biology discovery and immunohistochemistry validation in independent patient cohorts, we show that high expression of the apoptosis regulator Bcl-xL is associated with disease relapse specifically within BRAF mutant stage II/III colon cancer.\nC_LIO_LIThis data could enable tailored disease management to reduce relapse rates in the most aggressive subtype.\nC_LI

cancer biology↗

bioRxiv2017

QuPath: Open source software for digital pathology image analysis

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPaths flexible design make it suitable for a wide range of additional image analysis applications across biomedical research.

pathology↗