Single-cell physical phenotyping of mechanically dissociated tissue biopsies for fast diagnostic assessment

Rapid and accurate histopathological diagnosis during surgery is critical for clinical decision-making. The prevalent method of intraoperative consultation pathology is time, labour and cost intensive and requires the expertise of trained pathologists. Here, we present an alternative technique for the rapid, label-free analysis of biopsy samples by sequentially assessing the physical phenotype of singularized, suspended cells in high-throughput. This new diagnostic pipeline combines enzyme-free, mechanical dissociation of tissues with real-time deformability cytometry at measurement rates of 100 – 1,000 cells/sec, and machine learning-based analysis. We show that physical phenotype parameters extracted from brightfield images of single cells can be used to distinguish subpopulations of cells in various tissues, without prior knowledge or the need for molecular markers. Further, we demonstrate the potential of our method for inflammatory bowel disease diagnostics. Using unsupervised dimensionality reduction and logistic regression, we accurately differentiate between healthy and tumorous tissue in both mouse and human biopsy samples. The method delivers results within 30 minutes, laying the groundwork for a fast and marker-free diagnostic pipeline to detect pathological changes in solid biopsies.

Divalent magnesium restores cytoskeletal storage lesions in cold-stored platelet concentrates

Cold storage of platelet concentrates (PC) has become attractive due to the reduced risk of bacterial proliferation, but in vivo circulation time of cold-stored platelets is reduced. Ca2+ release from storage organelles and higher activity of Ca2+ pumps at temperatures < 15°C triggers cytoskeleton changes. This is suppressed by Mg2+ addition, avoiding a shift in Ca2+ hemostasis and cytoskeletal alterations. We report on the impact of 2–10 mM Mg2+ addition on cytoskeleton alterations of platelets from PC stored at room temperature (RT) or 4°C in additive solution (PAS), 30% plasma. Deformation of platelets was assessed by real-time deformability cytometry (RT-DC), a method for biomechanical cell characterization. Deformation was strongly affected by storage at 4°C and preserved by Mg2+ addition ≥ 4 mM Mg2+ (mean ± SD of median deformation 4°C vs. 4°C + 10mM Mg2+ 0.073 ± 0.021 vs. 0.118 ± 0.023, p < 0.01; n = 6, day 7). These results were confirmed by immunofluorescence microscopy, showing that Mg2+ ≥ 4mM prevents 4°C storage induced cytoskeletal structure lesion. Standard in vitro platelet function tests showed minor differences between RT and cold-stored platelets. Hypotonic shock response was reduced in cold-stored platelets (45.65 ± 11.59% vs. RT stored platelets 56.38 ± 29.36; p = 0.042) but normal at 4°C + 10 mM Mg2+ (55.22 ± 11.16%, all n = 6, day 1). CD62P expression and platelet aggregation response were similar between RT and 4°C stored platelets, with minor changes in the presence of higher Mg2+ concentrations. In conclusion, increasing Mg2+ up to 10 mM in PAS counteracts 4°C storage lesions in platelets, maintains platelet cytoskeletal integrity and biomechanical properties comparable to RT stored platelets.

Divalent magnesium restores cytoskeletal storage lesions in cold-stored platelet concentrates

Cold storage of platelet concentrates (PC) has become attractive due to the reduced risk of bacterial proliferation, but in vivo circulation time of cold-stored platelets is reduced. Ca2+ release from storage organelles and higher activity of Ca2+ pumps at temperatures < 15°C triggers cytoskeleton changes. This is suppressed by Mg2+ addition, avoiding a shift in Ca2+ hemostasis and cytoskeletal alterations. We report on the impact of 2–10 mM Mg2+ addition on cytoskeleton alterations of platelets from PC stored at room temperature (RT) or 4°C in additive solution (PAS), 30% plasma. Deformation of platelets was assessed by real-time deformability cytometry (RT-DC), a method for biomechanical cell characterization. Deformation was strongly affected by storage at 4°C and preserved by Mg2+ addition ≥ 4 mM Mg2+ (mean ± SD of median deformation 4°C vs. 4°C + 10mM Mg2+ 0.073 ± 0.021 vs. 0.118 ± 0.023, p < 0.01; n = 6, day 7). These results were confirmed by immunofluorescence microscopy, showing that Mg2+ ≥ 4mM prevents 4°C storage induced cytoskeletal structure lesion. Standard in vitro platelet function tests showed minor differences between RT and cold-stored platelets. Hypotonic shock response was reduced in cold-stored platelets (45.65 ± 11.59% vs. RT stored platelets 56.38 ± 29.36; p = 0.042) but normal at 4°C + 10 mM Mg2+ (55.22 ± 11.16%, all n = 6, day 1). CD62P expression and platelet aggregation response were similar between RT and 4°C stored platelets, with minor changes in the presence of higher Mg2+ concentrations. In conclusion, increasing Mg2+ up to 10 mM in PAS counteracts 4°C storage lesions in platelets, maintains platelet cytoskeletal integrity and biomechanical properties comparable to RT stored platelets.

AB0420 CIRCULATING MONOCYTES HAVE DISTINCT PHYSICAL PROPERTIES THAT CORRELATE WITH DISEASE ACTIVITY AND SEVERITY AND PREDICT PROGRESSION IN SYSTEMIC SCLEROSIS

Background:Systemic sclerosis (SSc) is associated with high morbidity and is one of the autoimmune rheumatic diseases with the highest mortality. However, tools to evaluate disease activity, response to treatment or to predict disease progression are scarce. Dysregulated immune responses are major pathogenic players at the onset and in the progression of SSc. Recent evidence demonstrates that mechanical properties of circulating leukocytes reflect their states and functions, and during activation ensure their adaptation to the changing physical requirements (e.g. softening to extravasate and migrate in the tissues) (1). Real-time fluorescence and deformability cytometry (RT-FDC) is a novel technique that allows the identification of cells from a heterogenous population by marker expression, with their subsequent mechanical phenotyping in a high-throughput manner (2, 3).Objectives:Here we characterized the physical properties of circulating immune cells in SSc patients, aiming to identify disease-related changes in their phenotypes, clinical correlates of these changes and their potential to predict disease progression.Methods:51 patients fulfilling the 2013 ACR/EULAR classification criteria for SSc and 17 age- and sex-matched healthy controls were included in the study. Blood was collected from the donors between 05.2019 and 10.2020. Peripheral blood mononuclear cells (PBMCs) were isolated and stained with antibodies against major circulating lymphoid (CD8+, CD4+ T cells, B cells, NK cells, NKT-like cells) and myeloid subpopulations (classical, intermediate and inflammatory monocytes, conventional dendritic cells and plasmacytoid dendritic cells). Each subpopulation was identified in RT-FDC by standard gating based on its marker expression and its area, deformation and apparent Young’s modulus (a measure of cell stiffness) were determined. The analysis was conducted using a custom Python script. For the patients included, demographic and clinical data were collected at every visit. Correlations with clinical parameters were analyzed in R.Results:All three subpopulations of monocytes identified by expression of HLA-DR, CD14 and/or CD16 had higher deformation and cross-sectional area in SSc patients as compared to healthy controls. From the SSc patients, monocytes had higher deformation and area in those with diffuse cutaneous SSc, extensive lung fibrosis and active disease as compared to those with limited cutaneous SSc, limited lung fibrosis and stable disease, respectively. Moreover, monocyte deformation and area significantly correlated with the EUSTAR activity index, with mRSS, with the extent of lung involvement on HR-CT (positive correlation), with DLCO and FVC (negative correlation). Follow-up data collected one year after the measurements showed that a higher monocyte deformation and cross-sectional area at baseline predicts future progression of lung disease, defined according to the INBUILD study, as well as future progression of skin fibrosis.Conclusion:We demonstrated that circulating subsets of monocytes in SSc patients show an increase in deformation and cross-sectional area, that these changes correlate with current disease activity and can identify patients with high risk of future progression of skin or lung fibrosis. These changes might reflect an activated state of circulating monocytes in SSc that facilitate their tissue migration. Mechanical phenotyping of monocytes by RT-FDC might thus serve as a useful tool for clinical evaluation of SSc patients.References:[1]Bashant KR, Toepfner N, Day CJ, Mehta NN, Kaplan MJ, Summers C, et al. The mechanics of myeloid cells. Biol Cell. 2020;112(4):103-12.[2]Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods. 2015;12(3):199-202, 4 p following.[3]Rosendahl P, Plak K, Jacobi A, Kraeter M, Toepfner N, Otto O, et al. Real-time fluorescence and deformability cytometry. Nat Methods. 2018;15(5):355-8.Disclosure of Interests:Alexandru-Emil Matei: None declared, Kubánková Markéta: None declared, Andrea-Hermina Györfi: None declared, Evgenia Boxberger: None declared, Despina Soteriou: None declared, Maria Papava: None declared, Julia Muth: None declared, Martin Kräter: None declared, Georg Schett: None declared, Jochen Guck: None declared, Jörg H.W. Distler Consultant of: Actelion, Active Biotech, Anamar, ARXX, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, GSK, Inventiva, JB Therapeutics, Medac, Pfizer, RuiYi and UCB, Grant/research support from: Anamar, Active Biotech, Array Biopharma, aTyr, BMS, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, GSK, Inventiva, Novartis, Sanofi-Aventis, RedX, UCB, Employee of: Stock owner of 4D Science and Scientific head of FibroCure

Deep Learning Assisted Mechanotyping of Individual Cells Through Repeated Deformations and Relaxations in Undulating Channels

Mechanical properties of cells are important features that are tightly regulated, and are dictated by various pathologies. Deformability cytometry allows for the characterization of mechanical properties of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that would assure classification accuracy approaching 100%. In order to maximize the accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations. We also track the shape dynamics of individual cells with high time resolution, and apply sequence-based deep learning models for feature extraction. HL60 cells with and without treatment with cytochalasin D (cytoD), a reagent previously shown to perturb the actin network, were used as a model system to understand the classification potential of our approach. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes, and shown to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified the two sub-populations of HL60 cells with an accuracy of 95%. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.

Microfluidic Assessment of Drug Effects on Physical Properties of Androgen Sensitive and Non-Sensitive Prostate Cancer Cells

The identification and treatment of androgen-independent prostate cancer are both challenging and significant. In this work, high-throughput deformability cytometry was employed to assess the effects of two anti-cancer drugs, docetaxel and enzalutamide, on androgen-sensitive prostate cancer cells (LNCaP) and androgen-independent prostate cancer cells (PC-3), respectively. The quantified results show that PC-3 and LNCaP present not only different intrinsic physical properties but also different physical responses to the same anti-cancer drug. PC-3 cells possess greater stiffness and a smaller size than LNCaP cells. As the docetaxel concentration increases, PC-3 cells present an increase in stiffness and size, but LNCaP cells only present an increase in stiffness. As the enzalutamide concentration increases, PC-3 cells present no physical changes but LNCaP cells present changes in both cell size and deformation. These results demonstrated that cellular physical properties quantified by the deformability cytometry are effective indicators for identifying the androgen-independent prostate cancer cells from androgen-sensitive prostate cancer cells and evaluating drug effects on these two types of prostate cancer.

Obstruction of Capillaries by circulating large Monocytes causes Multi Organ Dysfunction Syndrome (MODS) in Acute Pancreatitis

Acute pancreatitis (AP) is an inflammatory disorder, the severe form of which is burdened with high mortality. The pathogenesis of severity-driving organ manifestations, such as respiratory and renal failure, is unknown. We used samples from 300 pancreatitis patients and an experimental model of severe acute pancreatitis (SAP) to characterize severity-dependent cytokine profiles and resident/circulating immune cell populations by flow-cytometry and real-time- fluorescence and deformability-cytometry analysis. On functional, immunolabelling and in-vivo antibody-depletion experiments we confirmed the role of inflammatory cells in pancreatitis but found neither T-cells, Ly6g+-neutrophils or granulocytic-myeloid-derived-suppressor-cells (gMDSC) but a massive mobilisation of CCR2+/CD11b+-monocytes to be responsible for lung and kidney injury during SAP. Real-time-fluorescence and deformability-cytometry analyses suggest, that the physical properties of monocytes, especially their large size, results in an obstruction of the fine capillary-systems of the lung or of the kidney glomeruli. Their selective depletion can represent a promising treatment strategy for pancreatitis as well as other inflammation-related disorders.

De novo identification of universal cell mechanics regulators

Mechanical proprieties determine many cellular functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors governing cell mechanical phenotype is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between mechanical phenotype changes and the genes involved in driving them. We employ a machine learning-based discriminative network analysis method termed PC-corr to associate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large-scale transcriptome datasets ranging from stem cell development to cancer progression, and originating from different murine and human tissues. By intersecting the discriminative networks inferred from two selected datasets, we identify a conserved module of five genes with putative roles in the regulation of cell mechanics. We validate the power of the individual genes to discriminate between soft and stiff cell states in silico, and demonstrate experimentally that the top scoring gene, CAV1, changes the mechanical phenotype of cells when silenced or overexpressed. The data-driven approach presented here has the power of de novo identification of genes involved in cell mechanics regulation and paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.