Discovery of calcite as a new pro-inflammatory calcium-containing crystal in human osteoarthritic synovial fluid.

OBJECTIVE: We aimed to characterize calcium-containing crystals present in synovial fluid from patients with knee osteoarthritis (OA) using Raman spectroscopy, and specifically investigate the biological effects of calcite crystals. DESIGN: Thirty-two synovial fluid samples were collected pre-operatively from knee OA patients undergoing total joint arthroplasty. An integrated Raman polarized light microscope was used for identification of crystals in synovial fluid. Human peripheral blood mononuclear cells (PBMC's), human OA articular chondrocytes (HACs) and fibroblast-like synoviocytes (FLSs) were exposed to calcite crystals. Expression of relevant cytokines and inflammatory genes were measured using enzyme-linked immuno sorbent assay (ELISA) and real-time polymerase chain reaction (PCR). RESULTS: Various calcium-containing crystals were identified, including calcium pyrophosphate (37.5 %) and basic calcium phosphate (21.8 %), but they were never found simultaneously in the same OA synovial fluid sample. For the first time, we discovered the presence of calcite crystals in 93.8 % of the samples, while dolomite was detected in 25 % of the cases. Characterization of the cellular response to calcite crystal exposure revealed increased production of innate immune-derived cytokines by PBMC's, when co-stimulated with lipopolysaccharide (LPS). Additionally, calcite crystal stimulation of HACs and FLSs resulted in enhanced secretion of pro-inflammatory molecules and alterations in the expression of extracellular matrix remodeling enzymes. CONCLUSIONS: This study highlights the unique role of Raman spectroscopy in OA crystal research and identified calcite as a novel pro-inflammatory crystal type in OA synovial fluid. Understanding the role of specific crystal species in the OA joint may open new avenues for pharmacological interventions and personalized approaches to treating OA.

An objective diagnosis of gout and calcium pyrophosphate deposition disease with machine learning of Raman spectra acquired in a point-of-care setting.

OBJECTIVE: Raman spectroscopy is proposed as a next-generation method for the identification of monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals in synovial fluid. As the interpretation of Raman spectra requires specific expertise, the method is not directly applicable for clinicians. We developed an approach to demonstrate that the identification process can be automated with the use of machine learning techniques. The developed system is tested in a point-of-care-setting at our outpatient rheumatology department. METHODS: We collected synovial fluid samples from 446 patients with various rheumatic diseases from three centra. We analyzed all samples with our Raman spectroscope and used 246 samples for training and 200 samples for validation. Trained observers classified every Raman spectrum as MSU, CPP or else. We designed two one-against-all classifiers, one for MSU and one for CPP. These classifiers consisted of a principal component analysis model followed by a support vector machine. RESULTS: The accuracy for classification of CPP using the 2023 ACR/EULAR CPPD classification criteria was 96.0% (95% CI 92.3-98.3), while the accuracy for classification of MSU with using the 2015 ACR/EULAR gout classification criteria was 92.5% (95% CI 87.9-95.7). Overall, the accuracy for classification of pathological crystals was 88.0% (95% CI 82.7-92.2). The model was able to discriminate between pathologic crystals, artifacts, and other particles such as microplastics. CONCLUSION: We here demonstrate that potentially complex Raman spectra from clinical patient samples can be successfully classified by a machine learning approach, resulting in an objective diagnosis independent of the opinion of the medical examiner.

Calcium Pyrophosphate Crystal Formation and Deposition: Where Do we Stand and What Does the Future hold?

PURPOSE OF THE REVIEW: Although calcium pyrophosphate deposition (CPPD) has been known since the 1960s, our understanding of its pathogenesis remains rudimentary. This review aims to illustrate the known mechanisms underlying calcium pyrophosphate (CPP) crystal formation and deposition and explore future directions in research. By examining various perspectives, from basic research to clinical and imaging assessments, as well as new emerging methodologies, we can establish a starting point for a deeper understanding of CPPD pathogenesis. RECENT FINDINGS: Recent years have seen significant advances in CPPD research, particularly in the clinical field with the development of the 2023 ACR/EULAR classification criteria for CPPD disease, and in imaging with the introduction of the OMERACT ultrasonographic definitions and scoring system. However, progress in basic research has been slower. New laboratory approaches, such as Raman spectroscopy and omics sciences, offer promising insights that may help piece together the puzzle of CPPD. CPPD is a common yet understudied condition. As the population ages and CPPD becomes more prevalent, there is an urgent need to better understand the disease and the mechanisms involved in crystal formation and deposition, in order to improve diagnosis and therapeutic approaches.

Diagnostic Accuracy of Raman Spectroscopy Integrated With Polarized Light Microscopy for Calcium Pyrophosphate-Associated Arthritis.

OBJECTIVE: We studied the performance of integrated Raman polarized light microscopy (iRPolM) for the identification of calcium pyrophosphate (CPP)-associated arthritis (CPPD). METHODS: This is a diagnostic accuracy study including 400 consecutive synovial fluid samples from a single hospital in the Netherlands. Accuracy measures were calculated against polarized light microscopy (PLM) and the 2023 American College of Rheumatology (ACR)/EULAR criteria set for CPPD. RESULTS: The interrater reliability between iRPolM and the 2023 ACR/EULAR criteria set for CPPD was strong (κ = 0.88). The diagnostic performance of iRPolM compared to the 2023 ACR/EULAR criteria set was sensitivity 86.0% (95% confidence interval [CI] 73.3-94.2), specificity 99.1% (95% CI 97.5-99.8), positive likelihood ratio 100.33 (95% CI 32.3-311.3), negative likelihood ratio 0.14 (95% CI 0.07-0.28), positive predictive value 93.5% (95% CI 82.2-97.8), negative predictive value 98.0% (95% CI 82.2-97.8), and accuracy 97.5% (95% CI 95.5-98.8). We allowed rheumatologists to rate the certainty of their microscopic identification of CPP and found a large correspondence between iRPolM and a certain identification (κ = 0.87), whereas only 10% of the uncertain CPP identifications could be confirmed with iRPolM. We identified several novel particle types in synovial fluid analysis, including calcium carbonate crystals, deposited carotenoids, microplastics, and three types of Maltese cross birefringent objects. CONCLUSION: iRPolM can easily identify CPP crystals with a strong diagnostic performance. PLM alone is not specific enough to reliably resolve complicated cases, and the implementation of Raman spectroscopy in rheumatology practice can be of benefit to patients with suspected CPPD.

Test characteristics of Raman spectroscopy integrated with polarized light microscopy for the diagnosis of acute gouty arthritis.

OBJECTIVES: We studied the performance of Raman spectroscopy integrated with polarized light microscopy (iRPolM) as a next-generation technique for synovial fluid analysis in gout. METHODS: This is a prospective study, including consecutive synovial fluid samples drawn from any peripheral swollen joint. Diagnostic accuracy was compared to the 2015 ACR/EULAR Gout classification criteria as a reference test and to polarized light microscopy (PLM) analysis by a rheumatologist. Synovial fluid was analysed with iRPolM after unblinding the PLM results. RESULTS: Two hundred unselected consecutive patient samples were included in this study. Validation against clinical criteria: 67 patients were classified as gout according to 2015 ACR/EULAR classification criteria. Compared to the 2015 ACR/EULAR gout classification criteria, iRPolM had a sensitivity of 77.6% (95% CI: 65.8-86.9), specificity of 97.7% (95% CI: 93.5-99.5), positive predictive value (PPV) of 94.5% (95% CI: 84.9-98.2), negative predictive value (NPV) of 89.7% (95% CI: 84.7-93.1), an accuracy of 91.0% (95% CI: 86.2-94.6), a positive likelihood ratio of 34.4 (95% CI: 11.16-106.10) and a negative likelihood ratio of 0.23 (95% CI: 0.15-0.36). Validation against PLM: 55 samples were positive for MSU according to PLM. The interrater agreement between PLM and iRPolM was near perfect (к=0.90). The sensitivity of iRPolM to identify MSU in PLM-positive samples was 91.2% (95% CI: 80.7-97.1), the specificity was 97.6% (95% CI: 93.0-99.5), the PPV was 94.6% (95% CI: 85.0-98.2), NPV was 96.0% (95% CI: 91.2-98.2) and the accuracy was 95.6% (95% CI: 91.4-98.2). The positive likelihood ratio was 37.4 (95% CI: 12.20-114.71), and the negative likelihood ratio was 0.09 (95% CI: 0.04-0.21). CONCLUSION: iRPolM is a promising next-generation diagnostic tool for rheumatology by diagnosing gout with high specificity, increased objectivity, and a sensitivity comparable to PLM.

Optimal Halbach Configuration for Flow-through Immunomagnetic CTC Enrichment.

Due to the low frequency of circulating tumor cells (CTC), the standard CellSearch method of enumeration and isolation using a single tube of blood is insufficient to measure treatment effects consistently, or to steer personalized therapy. Using diagnostic leukapheresis this sample size can be increased; however, this also calls for a suitable new method to process larger sample inputs. In order to achieve this, we have optimized the immunomagnetic enrichment process using a flow-through magnetophoretic system. An overview of the major forces involved in magnetophoretic separation is provided and the model used for optimizing the magnetic configuration in flow through immunomagnetic enrichment is presented. The optimal Halbach array element size was calculated and both optimal and non-optimal arrays were built and tested using anti-EpCAM ferrofluid in combination with cell lines of varying EpCAM antigen expression. Experimentally measured distributions of the magnetic moment of the cell lines used for comparison were combined with predicted recoveries and fit to the experimental data. Resulting predictions agree with measured data within measurement uncertainty. The presented method can be used not only to optimize magnetophoretic separation using a variety of flow configurations but could also be adapted to optimize other (static) magnetic separation techniques.