Protocol for large scale whole blood immune monitoring by mass cytometry and Cyto Quality Pipeline

Support has been received (PI: M.E.A.) from the IMI2-JU project GA No 831434 (3TR) and IMI-JU project GA No 115565 (PRECISESADS). P.R. has received support from EMBO (7966) and from Consejería de Salud de Junta de Andalucía (EF-0091-2018). C.M. acknowledges funding from Programa Nicolas Monardes (C2-0002-2019). J.M.M. is funded by European Union-NextGenerationEU, Ministry of Universities (Spain’s Government) and the Recovery, Transformation and Resilience Plan. These results form a part of the P.R. PhD thesis in Biomedicine at the University of Granada. We are grateful to Olivia Santiago and Jose Diaz Cuéllar for technical support as a Core facility in Genyo research center. Also, we would like to express our gratitude to the donors. The figures in this paper were created with BioRender.comMass cytometry (MC) is a powerful large-scale immune monitoring technology. To maximize MC data quality, we present a protocol for whole blood analysis together with an R package, Cyto Quality Pipeline (CytoQP), which minimizes the experimental artifacts and batch effects to ensure data reproducibility. We describe the steps to stimulate, fix, and freeze blood samples before acquisition to make them suitable for retrospective studies. We then detail the use of bar-coding and reference samples to facilitate multicenter and multi-batch experiments.For complete details on the use and execution of this protocol, please refer to Rybakowska et al. (2021a) and (2021b).IMI2-JU project GA 831434IMI-JUproject GA 115565European Molecular Biology Organization (EMBO) 7966Junta de Andalucía EF-0091-2018Programa Nicolás Monardes C2-0002-2019European Union-NextGenerationEUMinistry of Universities (Spain's Government) and the Recovery, Transformation and Resilience Pla

Single-cell immune profiling of Meniere Disease patients

This work was supported by B-CTS-68-UGR20 Grant by FEDER Funds, PI17/1644 and PI20-1126 grants from ISCIII by FEDER Funds from the EU, CLINMON-2 from the Meniere's Society UK, and Impact Data Science (IMP0001) . MF is funded by F18/00228 grant from ISCIII by FEDER Funds from the EU. AEB is funded by the EU's Horizon 2020 Research and Innovation Programme, Grant Agreement Number 848261. LF is funded by CD20/0153 grant from ISCIII by FEDER Funds from the EU. Funding for open access charge: Universidad de Granada/CBUA.Background: Meniere Disease (MD) is an inner ear syndrome, characterized by episodes of vertigo, tinnitus and fluctuating sensorineural hearing loss. The pathological mechanism leading to sporadic MD is still poorly understood, however an allergic inflammatory response seems to be involved in some patients with MD. Objective: Decipher an immune signature associated with the syndrome. Methods: We performed mass cytometry immune profiling on peripheral blood from MD patients and controls. We analyzed differences in state and differences in abundance of the different cellular subsets. IgE levels were quantified through ELISA on supernatant of cultured whole blood. Results: We have identified two clusters of individuals according to the single cell cytokine profile. These clusters presented differences in IgE levels, immune cell population abundance, including a reduction of CD56dim NKcells, and changes in cytokine expression with a different response to bacterial and fungal antigens. Conclusion: Our results support a systemic inflammatory response in some MD patients that show a type 2 response with allergic phenotype, which could benefit from personalized IL-4 blockers.FEDER Funds B-CTS-68-UGR20, B-CTS-68-UGR20Instituto de Salud Carlos III Spanish Government PI17/1644, PI20-1126, CD20/0153, 848261EUMeniere's Society UKImpact Data Science F18/00228Horizon 2020 IMP0001Universidad de Granada/CBU

Lack of strong innate immune reactivity renders macrophages alone unable to control productive Varicella-Zoster Virus infection in an isogenic human iPSC-derived neuronal co-culture model.

peer reviewedWith Varicella-Zoster Virus (VZV) being an exclusive human pathogen, human induced pluripotent stem cell (hiPSC)-derived neural cell culture models are an emerging tool to investigate VZV neuro-immune interactions. Using a compartmentalized hiPSC-derived neuronal model allowing axonal VZV infection, we previously demonstrated that paracrine interferon (IFN)-α2 signalling is required to activate a broad spectrum of interferon-stimulated genes able to counteract a productive VZV infection in hiPSC-neurons. In this new study, we now investigated whether innate immune signalling by VZV-challenged macrophages was able to orchestrate an antiviral immune response in VZV-infected hiPSC-neurons. In order to establish an isogenic hiPSC-neuron/hiPSC-macrophage co-culture model, hiPSC-macrophages were generated and characterised for phenotype, gene expression, cytokine production and phagocytic capacity. Even though immunological competence of hiPSC-macrophages was shown following stimulation with the poly(dA:dT) or treatment with IFN-α2, hiPSC-macrophages in co-culture with VZV-infected hiPSC-neurons were unable to mount an antiviral immune response capable of suppressing a productive neuronal VZV infection. Subsequently, a comprehensive RNA-Seq analysis confirmed the lack of strong immune responsiveness by hiPSC-neurons and hiPSC-macrophages upon, respectively, VZV infection or challenge. This may suggest the need of other cell types, like T-cells or other innate immune cells, to (co-)orchestrate an efficient antiviral immune response against VZV-infected neurons

Functional mass cytometry for reclassification and precise diagnosis of systemic autoimmune diseases

El lupus eritematoso sistémico (LES), la artritis reumatoide (AR), la esclerosis sistémica (SSC), el síndrome de Sjögren (SSJ), la enfermedad mixta del tejido conectivo (MCTD) y el síndrome antifosfolípido primario (PAPS) se clasifican como enfermedades autoinmunes sistémicas (EAS o SAD en inglés). Estas enfermedades se caracterizan por signos de autoinmunidad que incluyen la producción de autoanticuerpos y el daño a diferentes órganos. Aunque tienen definiciones clínicas y criterios de diagnóstico clínico separados, estas enfermedades son difíciles de diagnosticar de manera diferencial, ya que los pacientes tienen síntomas muy superpuestos y signos clínicos variados, particularmente en las primeras etapas de la enfermedad. Este panorama clínico superpuesto impide el diagnóstico correcto y la administración temprana de fármacos. Si bien durante mucho tiempo se sospechó de la semejanza molecular entre las EAS, la falta de biomarcadores compartidos bien descritos dificulta el tratamiento y el diagnóstico. Por tanto, es necesario realizar estudios moleculares y celulares para clasificar a los pacientes en función del mecanismo fisiopatológico subyacente en una estrategia de medicina personalizada. Para estudiar la complejidad del sistema inmunológico a nivel de una sola célula, es necesario utilizar tecnologías adecuadas. La citometría de masas (Citometría por tiempo de vuelo, CyTOF, CM) es una técnica de alta dimensión que permite medir más de 50 marcadores en una sola célula. Por lo tanto, es una buena herramienta para realizar estudios de fenotipado profundo rastreando varios tipos de células o niveles de marcadores de activación celular. Sin embargo, para observar los patrones celulares específicos del paciente, es necesario reclutar números importantes de individuos, lo que a menudo involucra a diferentes centros de investigación. Por tanto, es necesario establecer un diseño experimental adecuado. La preservación de sangre completa es una forma atractiva de recolectar muestras de centros ubicados lejos de las instalaciones del centro donde se realiza la CM, sin embargo, hasta ahora no se han validado suficientes protocolos de preservación de sangre para CM. Además, debido a que las muestras adquiridas a través del instrumento CyTOF sufren obstrucción por el material celular así como caída de señal asociada a adquisiciones prolongadas y efectos de lote, se debe tener especial cuidado al analizar los datos cuando se estudian múltiples grupos de muestras. Por lo tanto, es necesario utilizar un flujo de análisis de datos que considere la normalización de datos, el control de calidad y la naturaleza de alta dimensión de los datos de CM. Además, para analizar cientos de muestras, el flujo de análisis debe adaptarse para estudios a gran escala e idealmente debe automatizarse tanto como sea posible. Sin embargo, hasta ahora, no se ha desarrollado tal flujo de trabajo con esas características. En esta tesis doctoral hemos estudiado 7 EAS diferentes con el fin de encontrar nuevos biomarcadores que permitan la reclasificación de pacientes según firmas de leucocitos circulan tes. Nuestro objetivo fue realizar un estudio de fenotipado profundo que incluya marcadores funcionales relevantes para las EAS. Como queríamos tener la imagen más completa del sistema inmunológico, decidimos recolectar muestras de sangre completa y usar citometría de masas para analizarlas. Para ello realizamos la recogida de muestras de sangre en diferentes centros ubicados en Granada y Córdoba. Por tanto, tuvimos que establecer un protocolo de criopreservación adecuado para estudios multicéntricos. Como en total se recolectaron más de cien muestras, también establecimos un protocolo experimental que minimiza la variación experimental, y se optimizó un proceso de análisis y control de calidad junto con el preprocesamiento de datos automatizado. Utilizando estos ajustes, hemos demostrado que los estudios de inmunofenotipificación de alto contenido se pueden realizar con éxito con pequeñas cantidades de sangre fijada / congelada. La fijación inmediata de sangre completa se beneficia de tiempos de manipulación más cortos, lo que evita la muerte celular, especialmente en el compartimento de neutrófilos. Diseñamos un flujo de trabajo experimental que limita la variación experimental y reportamos un flujo de trabajo de curación de datos basado en R que limpia los datos recolectados y corrige los efectos por lotes introducidos durante la preparación y tinción de la muestra. Este flujo está semiautomatizado y optimizado para estudios grandes que involucran fenotipado de sangre humana, junto con marcadores funcionales. Finalmente, demostramos que CM se puede utilizar con éxito para detectar grupos (clusters) de pacientes que tienen patrones inmunes similares, lo que respalda el desarrollo de la medicina personalizada en las EAS. Hemos construido un marco de reclasificación de pacientes utilizando frecuencias celulares y niveles de expresión de marcadores funcionales. Los cuatro grupos de pacientes identificados difieren en la frecuencia y el estado de activación de células mieloides y linfoides. Además, también se caracterizaron por diferentes niveles de citoquinas pro- y antiinflamatorias. Cada grupo contiene una mezcla de diferentes enfermedades, lo que confirma la alta heterogeneidad de cada etiqueta diagnóstica.Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSC), Sjögren’s syndrome (SJS), mixed connective tissue disease (MCTD) and primary antiphospholipid syndrome (PAPS) are classified as systemic autoimmune diseases (SADs). These diseases are characterized by signs of autoimmunity that include the production of autoantibodies and the damage of different organs. Although having separated clinical definitions and clinical diagnostic criteria, these diseases are difficult to diagnose differentially, as patients have highly overlapping symptoms and varied clinical signs, particularly at early disease stages. This overlapping clinical landscape impedes the correct diagnosis and early drug administration. While molecular resemblance between SADs was suspected for a long time, the lack of well described, shared biomarkers makes treatment and diagnosis difficult. Therefore, molecular and cellular-based studies need to be undertaken to classify the patients based on the physiopathological mechanism underlying the diseases in an strategy of personalized medicine. In order to study the complexity of the immune system at the single cell level, proper technologies need to be used. Mass cytometry (Cytometry by Time-Of-Flight, CyTOF, MC) is a high-dimensional technique that allows to measure more than 50 markers in one single cell. Thus, it is a good tool to perform deep-phenotyping studies tracking several cell types or levels of cellular activation markers. However, in order to observe patient-specific cellular patterns, significant amounts of individuals need to be recruited, involving often different research centers. Hence a proper experimental design needs to be established. Whole blood preservation seems to be an attractive way to gather samples from centers located far away from MC-core facility, yet not many blood-preservation protocols were validated for MC so far. Additionally, because samples acquired through the CyTOF instrument suffer from cell clogging, signal drop associated to long acquisition and batch effects, special care needs to be taken when analyzing the data when multiple groups of samples are studied. Thus, a data analysis pipeline that considers data normalization, quality control and the high-dimensional nature of MC data needs to be used. Additionally, in order to analyze hundreds of samples the analysis pipeline needs to be adapted for large-scale studies and ideally be automatized as much as possible. However up to now, no such workflow was developed. In this PhD thesis we studied 7 different SADs in order to find new biomarkers that allow for patient reclassification according to immune cell signatures. We aimed at performing a deep phenotyping study including functional markers relevant for SADs. As we wanted to have the most complete picture of the immune system we decided to collect whole blood samples and use MC cytometry to analyze them. In order to do this we collected blood samples in different centers located in Granada and Córdoba. Thus, we had to establish a cryopreservation protocol suitable for multicenter studies. As in total more than one hundred samples were collected, we established also an experimental protocol minimizing experimental variation, and a quality control and analysis pipeline was also optimized together with automatized data preprocessing. Using these settings we have demonstrated that high-content immunophenotyping studies can be successfully performed with small amounts of fixed/frozen blood. Immediate whole blood fixation benefits from shorter manipulation times, hence preventing cell death specially in the neutrophil compartment. We designed an experimental workflow that limits experimental variation and reported an R-based data curation workflow that cleans collected data and corrects the batch effects introduced during the sample preparation and staining. This pipeline is semi-automated and optimized for large studies involving human blood phenotyping, together with functional markers. Finally, we showed that MC can be successfully used to detect groups (clusters) of patients having similar immune landscapes, supporting the personalized medicine development in SADs. So far, we constructed a patient reclassification framework using cell frequencies and expression levels of functional markers. The four detected clusters differed in the frequency and activation state of both myeloid and lymphoid cells. Additionally, they were also characterized by different levels of pro and antiinflammatory cytokines. Each cluster contained a mixture of different diseases, confirming the high heterogeneity of each diagnosis label.Tesis Univ. Granada

Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.PR acknowledges support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° [115565], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution (MEAR as PI) and in particular the in-cash support from Sanofi/Genzyme to PR. CM was supported by Instituto de Salud Carlos III (Miguel Servet II program, CPII16/00028). The authors also acknowledge support from Instituto de Salud Carlos III (PI18/00082) partly supported by European FEDER funds.Ye

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.The authors also acknowledge funding from Consejería de la Salud y Familias de la Junta de Andalucía (PIER-0118-2019) and Instituto de Salud Carlos III (PI18/00082), partly supported by European FEDER funds.Ye

Stabilization of Human Whole Blood Samples for Multicenter and Retrospective Immunophenotyping Studies

Whole blood is often collected for large-scale immune monitoring studies to track changes in cell frequencies and responses using flow (FC) or mass cytometry (MC). In order to preserve sample composition and phenotype, blood samples should be analyzed within 24 h after bleeding, restricting the recruitment, analysis protocols, as well as biobanking. Herein, we have evaluated two whole blood preservation protocols that allow rapid sample processing and long-term stability. Two fixation buffers were used, Phosphoflow Fix and Lyse (BD) and Proteomic Stabilizer (PROT) to fix and freeze whole blood samples for up to 6 months. After analysis by an 8-plex panel by FC and a 26-plex panel by MC, manual gating of circulating leukocyte populations and cytokines was performed. Additionally, we tested the stability of a single sample over a 13-months period using 45 consecutive aliquots and a 34-plex panel by MC. We observed high correlation and low bias toward any cell population when comparing fresh and 6 months frozen blood with FC and MC. This correlation was confirmed by hierarchical clustering. Low coefficients of variation (CV) across studied time points indicate good sample preservation for up to 6 months. Cytokine detection stability was confirmed by low CVs, with some differences between fresh and fixed conditions. Thirteen months regular follow-up of PROT samples showed remarkable sample stability. Whole blood can be preserved for phenotyping and cytokine-response studies provided the careful selection of a compatible antibody panel. However, possible changes in cell morphology, differences in antibody affinity, and changes in cytokine-positive cell frequencies when compared to fresh blood should be considered. Our setting constitutes a valuable tool for multicentric and retrospective studies. (c) 2020 International Society for Advancement of Cytometr

PeacoQC : peak-based selection of high quality cytometry data

In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high-quality data that can be used for further downstream analysis. An R implementation is available