Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

Associations Between Rheumatoid Arthritis Clinical Factors and Synovial Cell Types and States.

OBJECTIVE: Recent studies have uncovered diverse cell types and states in the rheumatoid arthritis (RA) synovium; however, limited data exist correlating these findings with patient-level clinical information. Using the largest cohort to date with clinical and multicell data, we determined associations between RA clinical factors with cell types and states in the RA synovium. METHODS: The Accelerated Medicines Partnership Rheumatoid Arthritis study recruited patients with active RA who were not receiving disease-modifying antirheumatic drugs (DMARDs) or who had an inadequate response to methotrexate (MTX) or tumor necrosis factor inhibitors. RA clinical factors were systematically collected. Biopsies were performed on an inflamed joint, and tissue were disaggregated and processed with a cellular indexing of transcriptomes and epitopes sequencing pipeline from which the following cell type percentages and cell type abundance phenotypes (CTAPs) were derived: endothelial, fibroblast, and myeloid (EFM); fibroblasts; myeloid; T and B cells; T cells and fibroblasts (TF); and T and myeloid cells. Correlations were measured between RA clinical factors, cell type percentage, and CTAPs. RESULTS: We studied 72 patients (mean age 57 years, 75% women, 83% seropositive, mean RA duration 6.6 years, mean Disease Activity Score-28 C-reactive Protein 3 [DAS28-CRP3] score 4.8). Higher DAS28-CRP3 correlated with a higher T cell percentage (P < 0.01). Those receiving MTX and not a biologic DMARD (bDMARD) had a higher percentage of B cells versus those receiving no DMARDs (P < 0.01). Most of those receiving bDMARDs were categorized as EFM (57%), whereas none were TF. No significant difference was observed across CTAPs for age, sex, RA disease duration, or DAS28-CRP3. CONCLUSION: In this comprehensive screen of clinical factors, we observed differential associations between DMARDs and cell phenotypes, suggesting that RA therapies, more than other clinical factors, may impact cell type/state in the synovium and ultimately influence response to subsequent therapies.

Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability.

In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body's own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent ('dynamic') chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant ('cs-invariant') peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes.

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction1. There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2. Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments.

Clonally expanded CD38hi cytotoxic CD8 T cells define the T cell infiltrate in checkpoint inhibitor-associated arthritis.

Immune checkpoint inhibitor (ICI) therapies used to treat cancer, such as anti-PD-1 antibodies, can induce autoimmune conditions in some individuals. The T cell mechanisms mediating such iatrogenic autoimmunity and their overlap with spontaneous autoimmune diseases remain unclear. Here, we compared T cells from the joints of 20 patients with an inflammatory arthritis induced by ICI therapy (ICI-arthritis) with two archetypal autoimmune arthritides, rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Single-cell transcriptomic and antigen receptor repertoire analyses highlighted clonal expansion of an activated effector CD8 T cell population in the joints and blood of patients with ICI-arthritis. These cells were identified as CD38hiCD127- CD8 T cells and were uniquely enriched in ICI-arthritis joints compared with RA and PsA and also displayed an elevated interferon signature. In vitro, type I interferon induced CD8 T cells to acquire the ICI-associated CD38hi phenotype and enhanced cytotoxic function. In a cohort of patients with advanced melanoma, ICI therapy markedly expanded circulating CD38hiCD127- T cells, which were frequently bound by the therapeutic anti-PD-1 drug. In patients with ICI-arthritis, drug-bound CD8 T cells in circulation showed marked clonal overlap with drug-bound CD8 T cells from synovial fluid. These results suggest that ICI therapy directly targets CD8 T cells in patients who develop ICI-arthritis and induces an autoimmune pathology that is distinct from prototypical spontaneous autoimmune arthritides.

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 PAX5 low naïve 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.

Computer Vision Analysis of Rheumatoid Arthritis Synovium Reveals Lymphocytic Inflammation Is Associated With Immunoglobulin Skewing in Blood.

OBJECTIVE: We sought to develop computer vision methods to quantify aggregates of cells in synovial tissue and compare these with clinical and gene expression parameters. METHODS: We assembled a computer vision pipeline to quantify five features encompassing synovial cell density and aggregates and compared these with pathologist scores, disease classification, autoantibody status, and RNA expression in a cohort of 156 patients with rheumatoid arthritis (RA) and 149 patients with osteoarthritis (OA). RESULTS: All five features were associated with pathologist scores of synovial lymphocytic inflammation (P < 0.0001). Three features that related to the cells per unit of tissue were significantly increased in patients with both seronegative and seropositive RA compared with those with OA; on the other hand, aggregate features (number and diameter) were significantly increased in seropositive, but not seronegative, RA compared with OA. Aggregate diameter was associated with the gene expression of immunoglobulin heavy-chain genes in the synovial tissue. Compared with blood, synovial immunoglobulin isotypes were skewed from IGHM and IGHD to IGHG3 and IGHG1. Further, patients with RA with high levels of lymphocytic infiltrates in the synovium demonstrated parallel skewing in their blood with a relative decrease in IGHGM (P < 0.002) and IGHD (P < 0.03) and an increase in class-switched immunoglobulin genes IGHG3 (P < 0.03) and IGHG1 (P < 0.002). CONCLUSION: High-resolution automated identification and quantification of synovial immune cell aggregates uncovered skewing in the synovium from naïve IGHD and IGHM to memory IGHG3 and IGHG1 and revealed that this process is reflected in the blood of patients with high inflammatory synovium.

Drivers of heterogeneity in synovial fibroblasts in rheumatoid arthritis.

Inflammation of non-barrier immunologically quiescent tissues is associated with a massive influx of blood-borne innate and adaptive immune cells. Cues from the latter are likely to alter and expand activated states of the resident cells. However, local communications between immigrant and resident cell types in human inflammatory disease remain poorly understood. Here, we explored drivers of fibroblast-like synoviocyte (FLS) heterogeneity in inflamed joints of patients with rheumatoid arthritis using paired single-cell RNA and ATAC sequencing, multiplexed imaging and spatial transcriptomics along with in vitro modeling of cell-extrinsic factor signaling. These analyses suggest that local exposures to myeloid and T cell-derived cytokines, TNF, IFN-γ, IL-1ß or lack thereof, drive four distinct FLS states some of which closely resemble fibroblast states in other disease-affected tissues including skin and colon. Our results highlight a role for concurrent, spatially distributed cytokine signaling within the inflamed synovium.

Emerging synovial cell states in rheumatoid arthritis as potential therapeutic targets.

PURPOSE OF REVIEW: To summarize recently discovered novel cell states in rheumatoid arthritis (RA) synovium that could have important implications for disease treatment. RECENT FINDINGS: The use of multiomic technologies, including single-cell and spatial transcriptomics and mass cytometry, has led to the discovery of several novel cell states, which could have important implications for the treatment of RA. These cells can be found in patient blood, synovial fluid, or synovial tissue and span several immune cell subsets as well as stromal cell types. These diverse cell states may represent the targets of current or future therapeutics, while their fluctuations may inform the ideal timing for therapy. Future efforts are needed to implicate how each cell state functions in the pathophysiologic network within affected joints and how medications perturb each cell state and ultimately the tissue. SUMMARY: Multiomic molecular technologies have afforded the discovery of numerous novel cellular states in RA synovium; the next challenge will be to link these states to pathophysiology and treatment response.

Clinical and Histological Features of Prosthetic Joint Infections May Differ in Patients With Inflammatory Arthritis and Osteoarthritis.

Background: Patients with inflammatory arthritis are at increased risk of prosthetic joint infections (PJIs), but diagnosis in these patients can be challenging because active inflammatory arthritis produces elevated inflammatory markers that may mimic those seen in PJI. Purpose: In this pilot study, we sought to identify the clinical, microbiologic, and histopathologic features of culture-positive and culture-negative PJI in patients with inflammatory arthritis who underwent total hip arthroplasty (THA) or total knee arthroplasty (TKA). We also sought to obtain preliminary data to support a definitive study of optimal methods for PJI diagnosis in patients with inflammatory arthritis. Methods: We performed a retrospective analysis of TKA and THA patients treated for PJI from 2009 to 2018 at a single tertiary care orthopedic institution. Data were extracted from a longitudinally maintained hospital infection database. We reviewed hematoxylin and eosin slides of osteoarthritis and inflammatory arthritis PJI cases matched 3:1, respectively, by age, sex, and culture status. Clinical characteristics were evaluated using the Fisher exact test, χ2 test, Student t test, and Mann-Whitney U test where appropriate. Results: A total of 807 PJI cases were identified (36 inflammatory arthritis and 771 osteoarthritis cases). Patients with inflammatory arthritis presented younger, had a higher Charlson Comorbidity Index, more frequently used glucocorticoids, were more likely women, and had a higher proportion of culture-negative PJI compared with osteoarthritis patients. Of the 88 inflammatory arthritis cases reviewed for histopathology, a higher proportion of culture-positive than culture-negative PJI cases had >10 polymorphonuclear leucocytes per high-power field and met Musculoskeletal Infection Society criteria but presented with less chronic inflammation. Conclusions: This retrospective prognostic study suggests that culture-negative PJI may be more frequent in patients with inflammatory arthritis than in those with osteoarthritis. Chronic infections, antibiotic use, or misdiagnosis may be contributing factors to unclear PJI diagnoses among culture-negative cases. This preliminary work supports the need for further studies to assess the differences in clinical features between culture-negative and culture-positive PJI in patients with inflammatory arthritis and the ability of biological diagnostic markers to discriminate between them in this population.

The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis.

Synovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including a CD8+ GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation, and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here, we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues, using personalized reference genomes to mitigate technical confounding. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B, and T cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium.

Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.

Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation.

BACKGROUND: We sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples. METHODS: We compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs. RESULTS: Synovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82. CONCLUSIONS: H&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.

Cytotoxic CD8+ T cells target citrullinated antigens in rheumatoid arthritis.

The immune mechanisms that mediate synovitis and joint destruction in rheumatoid arthritis (RA) remain poorly defined. Although increased levels of CD8+ T cells have been described in RA, their function in pathogenesis remains unclear. Here we perform single cell transcriptome and T cell receptor (TCR) sequencing of CD8+ T cells derived from anti-citrullinated protein antibodies (ACPA)+ RA blood. We identify GZMB+CD8+ subpopulations containing large clonal lineage expansions that express cytotoxic and tissue homing transcriptional programs, while a GZMK+CD8+ memory subpopulation comprises smaller clonal expansions that express effector T cell transcriptional programs. We demonstrate RA citrullinated autoantigens presented by MHC class I activate RA blood-derived GZMB+CD8+ T cells to expand, express cytotoxic mediators, and mediate killing of target cells. We also demonstrate that these clonally expanded GZMB+CD8+ cells are present in RA synovium. These findings suggest that cytotoxic CD8+ T cells targeting citrullinated antigens contribute to synovitis and joint tissue destruction in ACPA+ RA.

Dynamic spectrum of ectopic lymphoid B cell activation and hypermutation in the RA synovium characterized by NR4A nuclear receptor expression.

Ectopic lymphoid structures (ELS) can develop in rheumatoid arthritis (RA) synovial tissue, but the precise pathways of B cell activation and selection are not well understood. Here, we identify a synovial B cell population characterized by co-expression of a family of orphan nuclear receptors (NR4A1-3), which is highly enriched in RA synovial tissue. A transcriptomic profile of NR4A synovial B cells significantly overlaps with germinal center light zone B cells and an accrual of somatic hypermutation that correlates with loss of naive B cell state. NR4A B cells co-express lymphotoxins α and ß and IL-6, supporting functions in ELS promotion. Expanded and shared clones between synovial NR4A B cells and plasma cells and the rapid upregulation with BCR stimulation point to in situ differentiation. Together, we identify a dynamic progression of B cell activation in RA synovial ELS, with NR4A transcription factors having an important role in local adaptive immune responses.

Neoplastic synovial lining cells that coexpress podoplanin and CD90 overproduce CSF-1, driving tenosynovial giant cell tumor.

Tenosynovial giant cell tumor (TCGT) is a rare neoplasm affecting the synovium of joints, bursae, and tendon sheaths. The overproduction of colony-stimulating factor-1 (CSF-1) by a minority of the tumor population works in a paracrine fashion to drive tumor growth. Pathology of the reactive, monocytic component has been well elucidated, whereas the populations of neoplastic cells and all the sources of CSF-1 overproduction are incompletely characterized. Podoplanin (PDPN), or gp38, is a cell surface glycoprotein that is expressed on fibroblast-like synovial cells and upregulated in rheumatoid arthritis and many cancers; it governs cell mobility, epithelial-mesenchymal transition, and other functions and is associated with lymphangiogenesis and poor prognosis in many solid tumors, which underscores its local and possible systemic effects. We found higher PDPN expression in TGCT than in internal controls of patients' healthy synovium. Flow cytometry partitioned PDPNhigh cells into PDPNhigh CD90+ and PDPNhigh CD14+ populations. Quantitative real-time polymerase chain reaction analysis of the PDPNhigh CD90+ cells revealed that CSF-1 expression was 10-fold higher than in PDPNhigh CD14+ cells. Therefore, we conclude that the lining fibroblast-like synovial cells, which express PDPNhigh CD90+ , are responsible for the overproduction of CSF-1 and for driving tumor growth.

Sequencing of Circulating Microbial Cell-Free DNA Can Identify Pathogens in Periprosthetic Joint Infections.

BACKGROUND: Over 1 million Americans undergo joint replacement each year, and approximately 1 in 75 will incur a periprosthetic joint infection. Effective treatment necessitates pathogen identification, yet standard-of-care cultures fail to detect organisms in 10% to 20% of cases and require invasive sampling. We hypothesized that cell-free DNA (cfDNA) fragments from microorganisms in a periprosthetic joint infection can be found in the bloodstream and utilized to accurately identify pathogens via next-generation sequencing. METHODS: In this prospective observational study performed at a musculoskeletal specialty hospital in the U.S., we enrolled 53 adults with validated hip or knee periprosthetic joint infections. Participants had peripheral blood drawn immediately prior to surgical treatment. Microbial cfDNA from plasma was sequenced and aligned to a genome database with >1,000 microbial species. Intraoperative tissue and synovial fluid cultures were performed per the standard of care. The primary outcome was accuracy in organism identification with use of blood cfDNA sequencing, as measured by agreement with tissue-culture results. RESULTS: Intraoperative and preoperative joint cultures identified an organism in 46 (87%) of 53 patients. Microbial cfDNA sequencing identified the joint pathogen in 35 cases, including 4 of 7 culture-negative cases (57%). Thus, as an adjunct to cultures, cfDNA sequencing increased pathogen detection from 87% to 94%. The median time to species identification for cases with genus-only culture results was 3 days less than standard-of-care methods. Circulating cfDNA sequencing in 14 cases detected additional microorganisms not grown in cultures. At postoperative encounters, cfDNA sequencing demonstrated no detection or reduced levels of the infectious pathogen. CONCLUSIONS: Microbial cfDNA from pathogens causing local periprosthetic joint infections can be detected in peripheral blood. These circulating biomarkers can be sequenced from noninvasive venipuncture, providing a novel source for joint pathogen identification. Further development as an adjunct to tissue cultures holds promise to increase the number of cases with accurate pathogen identification and improve time-to-speciation. This test may also offer a novel method to monitor infection clearance during the treatment period. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.