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.

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. 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 ELISA and real-time PCR. 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 peripheral blood mononuclear cells (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. 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.

The identification of synovial crystals is important for diagnosing rheumatic diseases. Currently, rheumatologists worldwide use compensated polarized light microscopy (CPLM) for crystal identification, but this technique is flawed. Raman spectroscopy might offer an objective, accurate alternative. We have tested Raman hyperspectral imaging on synovial fluid samples of 28 patients with swollen joints, measuring 5–10 crystals in each of these. Reference spectra for identification were measured using patient material, synthetic compounds, and oxalate kidney stones. Additionally, RRUFF and PANGAEA Raman databases were used to identify spectra. We identified the classical pathological crystals monosodium urate for gout and calcium pyrophosphate for calcium pyrophosphate deposition disease (CPPD). Hydroxyapatite, lipid spherules, and calcium oxalate monohydrate have also been observed and had been previously identified in synovial fluids. Other crystals, which were not previously observed in synovial fluid using CPLM, have been identified as well: calcite, aragonite, anatase, rutile, thenardite, dolomite, and a carotenoid. In addition, we found combinations of crystals in 13 out of 28 patients. We propose that the observed large variation of detectable crystals from a small population of patients and a small number of crystals per patient has significant ramifications for the use of compensated polarized light microscopy for the diagnosis of gout and CPPD. Further studies are required to learn the clinical significance of these crystals in human arthritis.

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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. Summary: 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.

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 centres. We analysed 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 other. 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 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 pathological 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.