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1. Beyond Autoantibodies: Biologic Roles of Human Autoreactive B Cells in Rheumatoid Arthritis Revealed by RNA‐Sequencing

   https://doi.org/10.1002/art.40772  

   Mahendra, Ankit ; Yang, Xingyu ; Abnouf, Shaza ; Adolacion, Jay R. T. ; Park, Daechan ; Soomro, Sanam ; Roszik, Jason ; Coarfa, Cristian ; Romain, Gabrielle ; Wanzeck, Keith ; et al ( February 2019 , Arthritis & Rheumatology)

   To obtain the comprehensive transcriptome profile of human citrulline‐specific B cells from patients with rheumatoid arthritis (RA).

   Citrulline‐ and hemagglutinin‐specific B cells were sorted by flow cytometry using peptide–streptavidin conjugates from the peripheral blood ofRApatients and healthy individuals. The transcriptome profile of the sorted cells was obtained byRNA‐sequencing, and expression of key protein molecules was evaluated by aptamer‐basedSOMAscan assay and flow cytometry. The ability of these proteins to effect differentiation of osteoclasts and proliferation and migration of synoviocytes was examined by in vitro functional assays.

   Citrulline‐specific B cells, in comparison to citrulline‐negative B cells, from patients withRAdifferentially expressed the interleukin‐15 receptor α (IL‐15Rα) gene as well as genes related to protein citrullination and cyclicAMPsignaling. In analyses of an independent cohort of cyclic citrullinated peptide–seropositiveRApatients, the expression ofIL‐15Rα protein was enriched in citrulline‐specific B cells from the patients’ peripheral blood, and surprisingly, all B cells fromRApatients were capable of producing the epidermal growth factor ligand amphiregulin (AREG). Production ofAREGdirectly led to increased migration and proliferation of fibroblast‐like synoviocytes, and, in combination with anti–citrullinated protein antibodies, led to the increased differentiation of osteoclasts.

   To the best of our knowledge, this is the first study to document the whole transcriptome profile of autoreactive B cells in any autoimmune disease. These data identify several genes and pathways that may be targeted by repurposing severalUSFood and Drug Administration–approved drugs, and could serve as the foundation for the comparative assessment of B cell profiles in other autoimmune diseases.

    

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2. High specificity of widely used phospho‐tau antibodies validated using a quantitative whole‐cell based assay

   https://doi.org/10.1111/jnc.14830  

   Li, Dan ; Cho, Yong Ku ( September 2019 , Journal of Neurochemistry)

   Antibodies raised against defined phosphorylation sites of the microtubule‐associated protein tau are widely used in scientific research and being applied in clinical assays. However, recent studies have revealed an alarming degree of non‐specific binding found in these antibodies. In order to quantify and compare the specificity phospho‐tau antibodies and other post‐translational modification site‐specific antibodies in general, a measure of specificity is urgently needed. Here, we report a robust flow cytometry assay using human embryonic kidney cells that enables the determination of a specificity parameter termed Φ, which measures the fraction of non‐specific signal in antibody binding. We validate our assay using anti‐tau antibodies with known specificity profiles, and apply it to measure the specificity of seven widely used phospho‐tau antibodies (AT270, AT8, AT100, AT180, PHF‐6, TG‐3, and PHF‐1) among others. We successfully determined the Φ values for all antibodies except AT100, which did not show detectable binding in our assay. Our results show that antibodies AT8, AT180, PHF‐6, TG‐3, and PHF‐1 have Φ values near 1, which indicates no detectable non‐specific binding. AT270 showed Φ value around 0.8, meaning that approximately 20% of the binding signal originates from non‐specific binding. Further analyses using immunocytochemistry and western blotting confirmed the presence of non‐specific binding of AT270 to non‐tau proteins found in human embryonic kidney cells and the mouse hippocampus. We anticipate that the quantitative approach and parameter introduced here will be widely adopted as a standard for reporting the specificity for phospho‐tau antibodies, and potentially for post‐translational modification targeting antibodies in general.

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   Cover Image for this issue: doi:10.1111/jnc.14727.

    

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3. Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

   https://doi.org/10.3390/cells12121638  

   Hladyshau, Siarhei ; Stoop, Jorik P. ; Kamada, Kosei ; Nie, Shuyi ; Tsygankov, Denis ( June 2023 , Cells)

   Rho-GTPases are central regulators within a complex signaling network that controls cytoskeletal organization and cell movement. The network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, along with their numerous effectors that provide mutual regulation through feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling, using a simulation model that couples GTPase signaling with cell morphodynamics and captures the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of time-lapsed recordings of cell dynamics and GTPase activity. Our data-driven modeling approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

    

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4. A Deep Learning Framework for Sickle Cell Disease Microfluidic Biomarker Assays

   https://doi.org/10.1182/blood-2020-141693  

   Praljak, Niksa ; Shipley, Brandon ; Gonzales, Ayesha ; Goreke, Utku ; Iram, Shamreen ; Singh, Gundeep ; Hill, Ailis ; Gurkan, Umut A. ; Hinczewski, Michael ( November 2020 , Blood)

   null (Ed.)

   Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCs under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neural networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding. 

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5. Arthritic Periosteal Tissue From Joint Replacement Surgery: A Novel, Autologous Source of Stem Cells

   https://doi.org/10.5966/sctm.2013-0056  

   Chang, Hana ; Docheva, Denitsa ; Knothe, Ulf R. ; Knothe Tate, Melissa L. ( January 2014 , Stem Cells Translational Medicine)

   The overarching aim of this study is to assess the feasibility of using periosteal tissue from the femoral neck of arthritic hip joints, usually discarded in the normal course of hip replacement surgery, as an autologous source of stem cells. In addition, the study aims to characterize intrinsic differences between periosteum-derived cell (PDC) populations, isolated via either enzymatic digestion or a migration assay, including their proliferative capacity, surface marker expression, and multipotency, relative to commercially available human bone marrow-derived stromal cells (BMSCs) cultured under identical conditions. Commercial BMSCs and PDCs were characterized in vitro, using a growth assay, flow cytometry, as well as assay of Oil Red O, alizarin red, and Safranin O/Fast Green staining after respective culture in adipo-, osteo-, and chondrogenic media. Based on these outcome measures, PDCs exhibited proliferation rate, morphology, surface receptor expression, and multipotency similar to those of BMSCs. No significant correlation was observed between outcome measures and donor age or diagnosis (osteoarthritis [OA] and rheumatoid arthritis [RA], respectively), a profound finding given recent rheumatological studies indicating that OA and RA share not only common biomarkers and molecular mechanisms but also common pathophysiology, ultimately resulting in the need for joint replacement. Furthermore, PDCs isolated via enzymatic digestion and migration assay showed subtle differences in surface marker expression but otherwise no significant differences in proliferation or multipotency; the observed differences in surface marker expression may indicate potential effects of isolation method on the population of cells isolated and/or the behavior of the respective isolated cell populations. This study demonstrates, for the first time to our knowledge, the feasibility of using arthritic tissue resected during hip replacement as a source of autologous stem cells. In sum, periosteum tissue that is resected with the femoral neck in replacing the hip represents an unprecedented and, to date, unstudied source of stem cells from OA and RA patients. Follow-up studies will determine the degree to which this new, autologous source of stem cells can be banked for future use.

    

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