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Dendritic cells (DCs) mature in an immunogenic or tolerogenic manner depending on the context in which an antigen is perceived, preserving the balance between immunity and tolerance. Whereas the pathways driving immunogenic maturation in response to infectious insults are well-characterized, the signals that drive tolerogenic maturation during homeostasis are still poorly understood. We found that the engulfment of apoptotic cells triggered homeostatic maturation of type 1 conventional DCs (cDC1s) within the spleen. This maturation process could be mimicked by engulfment of empty, nonadjuvanted lipid nanoparticles (LNPs), was marked by intracellular accumulation of cholesterol, and was highly specific to cDC1s. Engulfment of either apoptotic cells or cholesterol-rich LNPs led to the activation of the liver X receptor (LXR) pathway, which promotes the efflux of cellular cholesterol, and repressed genes associated with immunogenic maturation. In contrast, simultaneous engagement of TLR3 to mimic viral infection via administration of poly(I:C)-adjuvanted LNPs repressed the LXR pathway, thus delaying cellular cholesterol efflux and inducing genes that promote T cell–mediated immunity. These data demonstrate that conserved cellular cholesterol efflux pathways are differentially regulated in tolerogenic versus immunogenic cDC1s and suggest that administration of nonadjuvanted cholesterol-rich LNPs may be an approach for inducing tolerogenic DC maturation.

BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity.MethodsHere, we describe the ‘precisiononcology bysingle-cellprofiling usingex vivoreadouts of functionality’ (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution.ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34).Here,PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers uponex vivotreatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients’ samples this was more heterogeneous.ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

High-dimensional cytometry experiments measuring 20–50 cellular markers have become routine in many laboratories. The increased complexity of these datasets requires added rigor during the experimental planning and the subsequent manual and computational data analysis to avoid artefacts and misinterpretation of results. Here we discuss pitfalls frequently encountered during high-dimensional cytometry data analysis and aim to provide a basic framework and recommendations for reporting and analyzing these datasets.

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1–3 h to complete, though quality issues can increase this time considerably. This protocol describes FlowSOM, a clustering and visualization algorithm for unsupervised analysis of high-dimensional cytometry data. The protocol provides clearly annotated R code and an example dataset for inexperienced users.

The diagnostic work‐up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected‐MDS. The computational diagnostic workflow consists of methods for pre‐processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).

Antigen-presenting conventional dendritic cells (cDCs) are broadly divided into type 1 and type 2 subsets that further adapt their phenotype and function to perform specialized tasks in the immune system. The precise signals controlling tissue-specific adaptation and differentiation of cDCs are currently poorly understood. We found that mice deficient in the Ste20 kinase Thousand and One Kinase 3 (TAOK3) lacked terminally differentiated ESAM(+) CD4(+) cDC2s in the spleen and failed to prime CD4(+) T cells in response to allogeneic red-blood-cell transfusion. These NOTCH2- and ADAM10-dependent cDC2s were absent selectively in the spleen, but not in the intestine of Taok3(−/−) and CD11c-cre Taok3(fl/fl) mice. The loss of splenic ESAM(+) cDC2s was cell-intrinsic and could be rescued by conditional overexpression of the constitutively active NOTCH intracellular domain in CD11c-expressing cells. Therefore, TAOK3 controls the terminal differentiation of NOTCH2-dependent splenic cDC2s.

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

The mechanisms underlying operational tolerance after hematopoietic stem cell transplantation in humans are poorly understood. We studied two independent cohorts of patients who underwent allogeneic hematopoietic stem cell transplantation from human leukocyte antigen–identical siblings. Primary tolerance was associated with long-lasting reshaping of the recipients’ immune system compared to their healthy donors with an increased proportion of regulatory T cell subsets and decreased T cell activation, proliferation, and migration. Transcriptomics profiles also identified a role for nicotinamide adenine dinucleotide biosynthesis in the regulation of immune cell functions. We then compared individuals with operational tolerance and nontolerant recipients at the phenotypic, transcriptomic, and metabolomic level. We observed alterations centered on CD38+-activated T and B cells in nontolerant patients. In tolerant patients, cell subsets with regulatory functions were prominent. RNA sequencing analyses highlighted modifications in the tolerant patients’ transcriptomic profiles, particularly with overexpression of the ectoenzymeNT5E(encoding CD73), which could counterbalance CD38 enzymatic functions by producing adenosine. Further, metabolomic analyses suggested a central role of androgens in establishing operational tolerance. These data were confirmed using an integrative approach to evaluating the immune landscape associated with operational tolerance. Thus, balance between a CD38-activated immune state and CD73-related production of adenosine may be a key regulator of operational tolerance.

BACKGROUND Allogeneic hematopoietic cell transplantation (HCT) can be devastating when graft-versus-host disease (GvHD) develops. GvHD is characterized by mucosal inflammation due to breaching of epithelial barriers. Innate lymphoid cells (ILCs) are immune modulatory cells that are important in the maintenance of epithelial barriers, via their production of interleukin (IL)-22 and their T cell suppressive properties. After chemo- and radiotherapy, ILCs are depleted, and recovery after remission-induction therapy and after allogeneic HCT is slow and incomplete in a significant number of patients, which is associated with an increased risk to develop acute GvHD. OBJECTIVE To investigate whether the presence of mature ILCs within G-CSF-mobilized HCT grafts is correlated with the development of acute GvHD after allogeneic HCT. STUDY DESIGN We analyzed ILCs in a cohort of 36 patients who received allogeneic HCT for a hematologic malignancy, by flow-cytometric immune-phenotyping of prospectively collected, cryopreserved peripheral blood mononuclear cells (PBMCs) and donor-derived HCT grafts collected for the same patients. Biased analysis, with ILCs defined as CD3-lineage-CD45+CD127+CD161+ lymphocytes, was performed using FlowJo version 10 software. Unbiased analysis was done using FlowSOM, which uses a self-organizing map (SOM) with a minimal spanning tree (MST) to define and visualize different clusters present in the samples. RESULTS Remission-induction therapy significantly depleted ILCs from the blood, and patients who had a relatively low percentage of ILCs before allogeneic HCT were significantly more prone to develop acute GvHD, confirming previous findings in a separate cohort. Allogeneic HCT grafts, which were all obtained from the blood of G-CSF-mobilized healthy donors, contained ILCs at a frequency very similar to the peripheral blood of healthy individuals. The ILC subset composition was also comparable to that of the blood of healthy individuals, with the exception of NKp44+ ILC3s, which were significantly more abundant in HCT grafts. The relative ILC content of the graft tended to correlate with ILC reconstitution after allogeneic HCT, suggesting that peripheral expansion of transplanted mature ILCs may contribute to early ILC reconstitution after allogeneic HCT. Patients who received a relatively ILC-poor HCT graft had a significantly increased risk to develop acute GvHD, compared with patients who received relatively ILC-rich allogeneic HCT grafts. Unbiased phenotypic analysis with the FlowSOM algorithm confirmed that allogeneic HCT grafts of patients who developed acute GvHD contained a lower frequency of ILCs that clustered in NKp44+ ILC3 signature groups. CONCLUSION The presence of ILCs in allogeneic HCT grafts is associated with a reduced risk to develop acute GvHD. These data suggest that enhancement of ILC reconstitution of ILC3s in particular, for example via adoptive transfer of ILCs, may prevent acute GvHD and has the potential to improve outcome of allogeneic HCT recipients.

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.

Glioblastoma (GBM) remains a highly malignant and incurable brain tumour. The inability to achieve clinical improvements in GBM treatment can be attributed to the excessive heterogeneity and plasticity of GBM cells, which is reflected by the presence of various cellular states within each tumour. How each of these tumour cell subtypes respond to therapy remains largely unknown. In this work, we developed a functional diagnostic analysis pipeline to measure therapeutic activity in GBM tumour cells at single-cell resolution using mass cytometry by time-of-flight (CyTOF). By applying an optimised GBM-specific and therapy-tailored antibody panel, we measured therapeutic activity upon exposure to ionising radiation (RT) or a small molecule MDM2 inhibitor (AMG232) in a cohort of patient-derived GBM cell lines (n=14). As such, extended heterogeneity in drug responsiveness was reflected by diverse degrees of alterations in cell cycle progression and apoptotic signalling, in addition to shifts in tumoral phenotypic states implying therapy-induced plasticity. A similar approach was used to measure drug activity in freshly resected tumour samples (n=18) harvested from different tumour regions (core or invasive front) within hours following surgery. Accordingly, we identified highly variable fractions of responsive tumour and microenvironmental cell populations in a patient-specific way. The ability to measure drug activity at single-cell resolution in a patient-tailored manner by applying a genotype-agnostic method, paves the way for advanced precision cancer medicine in GBM by offering a novel approach to more precisely select eligible patients for prospective clinical trials.

High‐dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high‐content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross‐sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single‐cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population‐specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset‐specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch‐specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real‐world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package “CytoNorm” and available via the following link: http://www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

All authors: Andrea Cossarizza Hyun‐Dong Chang Andreas Radbruch Andreas Acs Dieter Adam Sabine Adam‐Klages William W. Agace Nima Aghaeepour Mübeccel Akdis Matthieu Allez Larissa Nogueira Almeida Giorgia Alvisi Graham Anderson Immanuel Andrä Francesco Annunziato Achille Anselmo Petra Bacher Cosima T. Baldari Sudipto Bari Vincenzo Barnaba Joana Barros‐Martins Luca Battistini Wolfgang Bauer Sabine Baumgart Nicole Baumgarth Dirk Baumjohann Bianka Baying Mary Bebawy Burkhard Becher Wolfgang Beisker Vladimir Benes Rudi Beyaert Alfonso Blanco Dominic A. Boardman Christian Bogdan Jessica G. Borger Giovanna Borsellino Philip E. Boulais Jolene A. Bradford Dirk Brenner Ryan R. Brinkman Anna E. S. Brooks Dirk H. Busch Martin Büscher Timothy P. Bushnell Federica Calzetti Garth Cameron Ilenia Cammarata Xuetao Cao Susanna L. Cardell Stefano Casola Marco A. Cassatella Andrea Cavani Antonio Celada Lucienne Chatenoud Pratip K. Chattopadhyay Sue Chow Eleni Christakou Luka Čičin‐Šain Mario Clerici Federico S. Colombo Laura Cook Anne Cooke Andrea M. Cooper Alexandra J. Corbett Antonio Cosma Lorenzo Cosmi Pierre G. Coulie Ana Cumano Ljiljana Cvetkovic Van Duc Dang Chantip Dang‐Heine Martin S. Davey Derek Davies Sara De Biasi Genny Del Zotto Gelo Victoriano Dela Cruz Michael Delacher Silvia Della Bella Paolo Dellabona Günnur Deniz Mark Dessing James P. Di Santo Andreas Diefenbach Francesco Dieli Andreas Dolf Thomas Dörner Regine J. Dress Diana Dudziak Michael Dustin Charles‐Antoine Dutertre Friederike Ebner Sidonia B. G. Eckle Matthias Edinger Pascale Eede Götz R.A. Ehrhardt Marcus Eich Pablo Engel Britta Engelhardt Anna Erdei Charlotte Esser Bart Everts Maximilien Evrard Christine S. Falk Todd A. Fehniger Mar Felipo‐Benavent Helen Ferry Markus Feuerer Andrew Filby Kata Filkor Simon Fillatreau Marie Follo Irmgard Förster John Foster Gemma A. Foulds Britta Frehse Paul S. Frenette Stefan Frischbutter Wolfgang Fritzsche David W. Galbraith Anastasia Gangaev Natalio Garbi Brice Gaudilliere Ricardo T. Gazzinelli Jens Geginat Wilhelm Gerner Nicholas A. Gherardin Kamran Ghoreschi Lara Gibellini Florent Ginhoux Keisuke Goda Dale I. Godfrey Christoph Goettlinger Jose M. González‐Navajas Carl S. Goodyear Andrea Gori Jane L. Grogan Daryl Grummitt Andreas Grützkau Claudia Haftmann Jonas Hahn Hamida Hammad Günter Hämmerling Leo Hansmann Goran Hansson Christopher M. Harpur Susanne Hartmann Andrea Hauser Anja E. Hauser David L. Haviland David Hedley Daniela C. Hernández Guadalupe Herrera Martin Herrmann Christoph Hess Thomas Höfer Petra Hoffmann Kristin Hogquist Tristan Holland Thomas Höllt Rikard Holmdahl Pleun Hombrink Jessica P. Houston Bimba F. Hoyer Bo Huang Fang‐Ping Huang Johanna E. Huber Jochen Huehn Michael Hundemer Christopher A. Hunter William Y. K. Hwang Anna Iannone Florian Ingelfinger Sabine M Ivison Hans‐Martin Jäck Peter K. Jani Beatriz Jávega Stipan Jonjic Toralf Kaiser Tomas Kalina Thomas Kamradt Stefan H. E. Kaufmann Baerbel Keller Steven L. C. Ketelaars Ahad Khalilnezhad Srijit Khan Jan Kisielow Paul Klenerman Jasmin Knopf Hui‐Fern Koay Katja Kobow Jay K. Kolls Wan Ting Kong Manfred Kopf Thomas Korn Katharina Kriegsmann Hendy Kristyanto Thomas Kroneis Andreas Krueger Jenny Kühne Christian Kukat Désirée Kunkel Heike Kunze‐Schumacher Tomohiro Kurosaki Christian Kurts Pia Kvistborg Immanuel Kwok Jonathan Landry Olivier Lantz Paola Lanuti Francesca LaRosa Agnès Lehuen Salomé LeibundGut‐Landmann Michael D. Leipold Leslie Y.T. Leung Megan K. Levings Andreia C. Lino Francesco Liotta Virginia Litwin Yanling Liu Hans‐Gustaf Ljunggren Michael Lohoff Giovanna Lombardi Lilly Lopez Miguel López‐Botet Amy E. Lovett‐Racke Erik Lubberts Herve Luche Burkhard Ludewig Enrico Lugli Sebastian Lunemann Holden T. Maecker Laura Maggi Orla Maguire Florian Mair Kerstin H. Mair Alberto Mantovani Rudolf A. Manz Aaron J. Marshall Alicia Martínez‐Romero Glòria Martrus Ivana Marventano Wlodzimierz Maslinski Giuseppe Matarese Anna Vittoria Mattioli Christian Maueröder Alessio Mazzoni James McCluskey Mairi McGrath Helen M. McGuire Iain B. McInnes Henrik E. Mei Fritz Melchers Susanne Melzer Dirk Mielenz Stephen D. Miller Kingston H.G. Mills Hans Minderman Jenny Mjösberg Jonni Moore Barry Moran Lorenzo Moretta Tim R. Mosmann Susann Müller Gabriele Multhoff Luis Enrique Muñoz Christian Münz Toshinori Nakayama Milena Nasi Katrin Neumann Lai Guan Ng Antonia Niedobitek Sussan Nourshargh Gabriel Núñez José‐Enrique O'Connor Aaron Ochel Anna Oja Diana Ordonez Alberto Orfao Eva Orlowski‐Oliver Wenjun Ouyang Annette Oxenius Raghavendra Palankar Isabel Panse Kovit Pattanapanyasat Malte Paulsen Dinko Pavlinic Livius Penter Pärt Peterson Christian Peth Jordi Petriz Federica Piancone Winfried F. Pickl Silvia Piconese Marcello Pinti A. Graham Pockley Malgorzata Justyna Podolska Zhiyong Poon Katharina Pracht Immo Prinz Carlo E. M. Pucillo Sally A. Quataert Linda Quatrini Kylie M. Quinn Helena Radbruch Tim R. D. J. Radstake Susann Rahmig Hans‐Peter Rahn Bartek Rajwa Gevitha Ravichandran Yotam Raz Jonathan A. Rebhahn Diether Recktenwald Dorothea Reimer Caetano Reis e Sousa Ester B.M. Remmerswaal Lisa Richter Laura G. Rico Andy Riddell Aja M. Rieger J. Paul Robinson Chiara Romagnani Anna Rubartelli Jürgen Ruland Armin Saalmüller Yvan Saeys Takashi Saito Shimon Sakaguchi Francisco Sala‐de‐Oyanguren Yvonne Samstag Sharon Sanderson Inga Sandrock Angela Santoni Ramon Bellmàs Sanz Marina Saresella Catherine Sautes‐Fridman Birgit Sawitzki Linda Schadt Alexander Scheffold Hans U. Scherer Matthias Schiemann Frank A. Schildberg Esther Schimisky Andreas Schlitzer Josephine Schlosser Stephan Schmid Steffen Schmitt Kilian Schober Daniel Schraivogel Wolfgang Schuh Thomas Schüler Reiner Schulte Axel Ronald Schulz Sebastian R. Schulz Cristiano Scottá Daniel Scott‐Algara David P. Sester T. Vincent Shankey Bruno Silva‐Santos Anna Katharina Simon Katarzyna M. Sitnik Silvano Sozzani Daniel E. Speiser Josef Spidlen Anders Stahlberg Alan M. Stall Natalie Stanley Regina Stark Christina Stehle Tobit Steinmetz Hannes Stockinger Yousuke Takahama Kiyoshi Takeda Leonard Tan Attila Tárnok Gisa Tiegs Gergely Toldi Julia Tornack Elisabetta Traggiai Mohamed Trebak Timothy I.M. Tree Joe Trotter John Trowsdale Maria Tsoumakidou Henning Ulrich Sophia Urbanczyk Willem van de Veen Maries van den Broek Edwin van der Pol Sofie Van Gassen Gert Van Isterdael René A.W. van Lier Marc Veldhoen Salvador Vento‐Asturias Paulo Vieira David Voehringer Hans‐Dieter Volk Anouk von Borstel Konrad von Volkmann Ari Waisman Rachael V. Walker Paul K. Wallace Sa A. Wang Xin M. Wang Michael D. Ward Kirsten A Ward‐Hartstonge Klaus Warnatz Gary Warnes Sarah Warth Claudia Waskow James V. Watson Carsten Watzl Leonie Wegener Thomas Weisenburger Annika Wiedemann Jürgen Wienands Anneke Wilharm Robert John Wilkinson Gerald Willimsky James B. Wing Rieke Winkelmann Thomas H. Winkler Oliver F. Wirz Alicia Wong Peter Wurst Jennie H. M. Yang Juhao Yang Maria Yazdanbakhsh Liping Yu Alice Yue Hanlin Zhang Yi Zhao Susanne Maria Ziegler Christina Zielinski Jakob Zimmermann Arturo Zychlinsky., These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion., This work was supported by the Netherlands Organisation for Scientific Research – Domain Applied and Engineering Sciences (NWO-TTW), research program VENI 15924. This work was funded by the Deutsche Forschungsgemeinschaft. European Union Innovative Medicines Initiative - Joint Undertaking - RTCure Grant Agreement 777357 and innovation program (Grant Agreement 695551).

Simple Summary Currently available biomarkers for response to checkpoint inhibitors are incomplete and predominantly focus on tumor tissue analysis e.g., tumor mutational burden, programmed cell death-ligand 1 (PD-L1) expression. Biomarkers in peripheral blood would allow a more dynamic monitoring and could offer a way for sequential adaptation of treatment strategy. We conducted an in-depth analysis of baseline and on-treatment systemic immune features in a cohort of stage III/IV melanoma and stage IV urothelial cancer (UC) patients treated with anti-programmed cell death-1 (anti-PD-1) therapy combined with stereotactic body radiotherapy (SBRT) in a similar regimen/schedule. Baseline immunity was clearly different between these two cohorts, indicating a less active immune landscape in UC patients. This study also detected signatures of proliferation in the CD8+ T-cell compartment pre-treatment and early after anti-PD-1 initiation that were positively correlated with clinical outcome in both tumor types. In addition our data support the biological relevance of PD-1/PD-L1 expression on circulating immune cell subsets, especially in melanoma. Abstract (1) Background: Blockade of the PD-1/PD-L1 pathway has revolutionized the oncology field in the last decade. However, the proportion of patients experiencing a durable response is still limited. In the current study, we performed an extensive immune monitoring in patients with stage III/IV melanoma and stage IV UC who received anti-PD-1 immunotherapy with SBRT. (2) Methods: In total 145 blood samples from 38 patients, collected at fixed time points before and during treatment, were phenotyped via high-parameter flow cytometry, luminex assay and UPLC-MS/MS. (3) Results: Baseline systemic immunity in melanoma and UC patients was different with a more prominent myeloid compartment and a higher neutrophil to lymphocyte ratio in UC. Proliferation (Ki67+) of CD8+ T-cells and of the PD-1+/PD-L1+ CD8+ subset at baseline correlated with progression free survival in melanoma. In contrast a higher frequency of PD-1/PD-L1 expressing non-proliferating (Ki67−) CD8+ and CD4+ T-cells before treatment was associated with worse outcome in melanoma. In UC, the expansion of Ki67+ CD8+ T-cells and of the PD-L1+ subset relative to tumor burden correlated with clinical outcome. (4) Conclusion: This study reveals a clearly different immune landscape in melanoma and UC at baseline, which may impact immunotherapy response. Signatures of proliferation in the CD8+ T-cell compartment prior to and early after anti-PD-1 initiation were positively correlated with clinical outcome in both cohorts. PD-1/PD-L1 expression on circulating immune cell subsets seems of clinical relevance in the melanoma cohort.

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1-3 h to complete, though quality issues can increase this time considerably.

Introduction Flow cytometry is a recommended tool in the diagnostic work-up of cytopenic patients suspected for myelodysplastic syndromes. Currently used flow cytometry scores rely on human assessment of dysplastic features in the bone marrow. Although proven useful, these methods are labor intensive and require a high level of expertise. Therefore, we previously developed a machine learning-based workflow for flow cytometry diagnostics in MDS by combining computational cell detection and a machine learning-classifier. This workflow outperformed traditional diagnostic scores with respect to accuracy (sensitivity 85-97%, specificity 93-97%), time investment ( Method Patient inclusion and characteristics Very low to intermediate risk MDS patients enrolled in the HOVON89 clinical trial (EudraCT 2008-002195-10) were included. 53 patients met the additional inclusion criteria, concerning written consent for add-on studies and availability of required flow cytometry data. Sample preparation Bone marrow samples were processed for flow cytometry analysis according to the European Leukemia Net guidelines. This study focused on the antibody combination optimized for assessment of myeloid progenitors and erythroid dysplasia (CD45, CD34, CD117, HLA-DR, CD71, CD36, CD105, CD33, sideward light scatter (SSC) and forward light scatter (FSC)). Machine learning-based workflow The machine learning-based workflow was developed in a prior study based on a reference cohort consisting of MDS patients without excess of blasts(n=67) and non-MDS cases (n=81) (Figure 1). MDS patients were diagnosed based on (cyto)morphology, cytogenetics and clinical follow-up. Non-MDS cases were patients with confirmed non-neoplastic cytopenias (n=69) and age-matched healthy individuals (n=12). Results In the validation cohort, the machine learning-based diagnostic workflow classified 49 out of 53 patients correctly, reaching a sensitivity of 92%. The workflow outperformed two currently used diagnostic tools for MDS flow cytometry, the Ogata score and integrated flow cytometry score (iFS). The former obtained 72% sensitivity (McNemar: p = 0.001) and the latter 83% sensitivity (McNemar: p = 0.06) in the validation cohort. Per patient, time required for automated analysis was less than 30 seconds. All four MDS patients that classified false negatively had a normal karyotype and (very) low risk disease according to the IPSS-r. In three out of four patients, no mutations or MDS-associated immunophenotypic features were detected. One patients was diagnosed as MDS-MLD and three patients as MDS-RS-SLD according to the WHO 2016 classification. The ten most relevant cellular features that discriminated between MDS and non-MDS patients in the reference data were confirmed in the current validation cohort. All ten features of MDS patients in the validation cohort were significantly different from non-MDS patients of the reference cohort (all features, p < 0.00001) (Figure 2). Seven out of ten features were similar in MDS patients of the validation cohort compared to those of the MDS patients of the reference cohort (p>0.05) (Figure 2). Conclusion In this validation study, we confirmed accuracy of machine learning-based flow cytometry diagnostics in lower risk MDS. The workflow obtained 92% sensitivity, which is in accordance with results from our previous study (85-97%), and outperformed currently used diagnostic flow cytometry scores for MDS (i.e. Ogata score and iFS). In our previous study specificity was 95% in both reference and test cohorts. Cellular features, most discriminative for diagnosis, were confirmed in the validation cohort, emphasizing robustness of the method. Additional benefits of this approach are the reduction in analysis time to less than thirty seconds per patient, reduction of required antibodies and increased reproducibility. Disclosures van de Loosdrecht: celgene: Honoraria; novartis: Honoraria.

Tumors of various histological origins show abundant infiltration of myeloid cells from early stages of disease progression. These cells have a profound impact on antitumor immunity and influence fundamental processes that underlie malignancy, including neoangiogenesis, sustained cancer cell proliferation, metastasis and therapy resistance. For these reasons, development of therapeutic approaches to deplete or reprogram myeloid cells in cancer is an emerging field of interest. However, knowledge about the heterogeneity of myeloid cells in tumors and their variability between patients and disease stages is still limited. In this review, we summarize the most recent advances in our understanding about how the phenotype of tumor-associated macrophages, monocytes, neutrophils, myeloid-derived suppressor cells and dendritic cells is dictated by their ontogeny, activation status and localization. We also outline major open questions that will only be resolved by applying high-dimensional single-cell technologies and systems biology approaches in the analysis of the tumor microenvironment.

BACKGROUND: Genome-wide association studies in asthma have repeatedly identified single nucleotide polymorphisms in the ORM (yeast)-like protein isoform 3 (ORMDL3) gene across different populations. Although the ORM-homologues in yeast are well-known inhibitors of sphingolipid (SL) synthesis, it is still unclear whether and how mammalian ORMDL3 regulates SL metabolism and whether altered SL synthesis would be causally related to asthma risk. OBJECTIVE: To examine the in vivo role of ORMDL3 in SL metabolism and allergic asthma METHODS: Ormdl3-LacZ reporter mice, gene deficient Ormdl3(-/-) and overexpressing Ormdl3 (Tg/wt) mice were exposed to physiologically relevant aeroallergens such as house dust mite or Alternaria alternata to induce experimental asthma. Mass spectrometry based sphingolipidomics was performed, and airway eosinophilia, Th2 cytokine production, Ig synthesis, airway remodeling and bronchial hyperreactivity measured. RESULTS: HDM challenge significantly increased total sphingolipids in the lung of HDM-sensitized mice compared to controls. In Ormdl3(Tg/wt) mice, the allergen-induced increase in lung ceramides was significantly reduced, whereas total sphingolipid levels were not affected. Conversely, in liver and serum, total sphingolipids including ceramides were increased in Ormdl3(-/-) mice, while they were lowered in Ormdl3(Tg/wt) mice. This difference was independent of allergen exposure. Despite these changes, all features of asthma were identical between wildtype, Ormdl3(Tg/wt) and Ormdl3(-/-) mice, across several models of experimental asthma. CONCLUSION: ORMDL3 regulates systemic ceramide levels, but genetically interfering with Ormdl3. expression does not result in altered experimental asthma. CAPSULE SUMMARY: ORMDL3 regulates systemic ceramide levels, but genetically interfering with Ormdl3 expression does not result in altered experimental asthma.

Population-wide assessment of the human immune system is needed to better understand immune homeostasis, immune responses against pathogens or the efficacy of vaccines. High-dimensional flow cytometry is the gold standard for in-depth immunophenotyping but the assessment of large cohorts requires multiple experiments over a prolonged period of time and bear multiple sources of error, reducing precision required for sensitive analyses. We performed a large immunophenotyping study using 28-color flow cytometry to investigate the heritability and homeostasis of the immune system and T cell functions in 3000 individuals. Here, we describe our high-throughput sample and data processing pipeline (200 samples/day) in combination with high dimensional flow cytometry to dramatically reduce the number of experiment-days in our study, resulting in low variation and high reproducibility. We demonstrate several carefully optimized strategies to reduce experimental errors and inter-assay variation at every level. In addition, we discuss strategies for pre-processing and analysis of large flow cytometry datasets using manual gating and unsupervised clustering analyses, such as FlowSOM, in view of the fact that most current strategies are highly limited for large datasets. In conclusion, we established a high-throughput high-dimensional flow cytometry pipeline enabling the analysis of thousands of samples with low technical and experimental variation. Our pipeline in combination with our three 28-color flow cytometry panels is adaptable to other large cohort studies for which precision and accuracy over time are important.

Summary Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients., Graphical Abstract Image 1, Highlights • A conserved gating strategy aligns dendritic cells (DCs) in mouse and human tissues • Unsupervised computational analysis of flow cytometry data outperforms manual analysis • Mass cytometry reveals heterogeneity of DC subsets across mouse and human tissues • DC activation upon inflammation tracked by automated analysis of mass cytometry, Using unsupervised analysis of flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues, Guilliams et al. provide a universal toolbox for the automated identification of dendritic cells. This framework represents the way forward to high-throughput and standardized analysis of dendritic cells from mutant mice and patients.

International audience; Interferon regulatory factor-8 (IRF8) has been proposed to be essential for development of monocytes, plasmacytoid dendritic cells (pDCs) and type 1 conventional dendritic cells (cDC1s) and remains highly expressed in differentiated DCs. Transcription factors that are required to maintain the identity of terminally differentiated cells are designated `' terminal selectors.'' Using BM chimeras, conditional Irf8(fl/fl) mice and various promotors to target Cre recombinase to different stages of monocyte and DC development, we have identified IRF8 as a terminal selector of the cDC1 lineage controlling survival. In monocytes, IRF8 was necessary during early but not late development. Complete or late deletion of IRF8 had no effect on pDC development or survival but altered their phenotype and gene-expression profile leading to increased T cell stimulatory function but decreased type 1 interferon production. Thus, IRF8 differentially controls the survival and function of terminally differentiated monocytes, cDC1s, and pDCs.

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.

Background. Treatment failure after allogeneic haematopoietic stem-cell transplantation (AHST) using reduced-intensity conditioning (RIC) results from too much alloreactivity and harmful acute Graft-versus-Host Disease (aGVHD). Studies have identified many reconstituting immune cell subsets associated with development of clinical alloreactivity but the functionally dominant parameters at different time-points remain unknown. We therefore used mass cytometry (MS) to simultaneously assess multiple alloreactive and immunoregulatory cell populations to identify dominant immune reconstitution signatures associated with subsequent development of aGvHD after AHST. Methods. Phenotypic markers identifying more than 30 immune cell subsets known to influence alloreactivity were combined in a single MS panel. Peripheral blood from 58 patients with haematological cancers was analysed after T-replete HLA-matched RIC-AHST using uniform conditioning. Normalization of individual test samples spiked with CD45-barcoded healthy control cells was used to reduce batch effects. Complementary high-dimensional analytic tools were used to generate cellular profiles across the whole cohort and identify differences between patients grouped by subsequent development of aGVHD. Results. Unsupervised clustering analysis identified 40 phenotypically distinct T, B and NK cell clusters post-transplant. Significant batch effects were effectively reduced with a novel R-based algorithm normalising data to control cells. Cluster diversity analysis early post-transplant demonstrated lower cluster diversity in patients who subsequently developed aGvHD consistent with perturbation of phenotypic clusters in these patients. Two specific clusters were significantly different in abundance at D+30 in patients who went on to develop aGvHD and those who remained aGVHD-free. A cluster with a CD56brightCD16negCD27+/- regulatory NK cell (NKreg) phenotype was reduced in patients going on to develop aGvHD using both Phenograph and FlowSOM algorithms (p=0.001). These findings were validated by forward analysis using the CITRUS algorithm, revealing a similar differentiating cell population. CD56bright NKreg reconstitution was independent of CMV reactivation and did not impede reconstitution of WT1 and PR1 tumor-associated antigen-specific T cells. The reduction in NKreg in patients who subsequently developed aGvHD was accompanied by a significant increase in alloreactive CCR5+CD45RA-CCR7- CD4 effector memory T cells (Tem). We next used correlation analysis of cluster abundance across the whole cohort to identify all clusters contributing to the immune 'regulome' (those inversely correlated with alloreactive CD4 EM and/or CD8 EM T cell clusters). Notably, at D+30 the regulome consisted of 4 phenotypically distinct CD56bright NKreg clusters and a CD4+CD8+ double positive (DP) cluster, but not FOXP3+ CD4 regulatory T cells (Treg), Figure 1A Both the identity of differentiating clusters between patients subsequently developing aGVHD and those who remained aGvHD-free, and the dominant constituents of the regulome changed over time. By D+60 a CD56bright NKreg cluster (with a distinct phenotype to the differentiating cluster identified at D+30) and a DP T cell cluster were significantly reduced in patients subsequently developing aGvHD. The D+60 regulome consisted of multiple distinct CD56bright clusters, a DP T cell cluster and CD4 Treg, Figure 1B. Importantly by D+90 the immune regulome consisted of a reduced number of CD56bright NKreg clusters and increasing dominance of CD4Treg, Figure 1C. Conclusion. We show proof-of-concept that a novel acquisition and analysis pipeline can be applied to MS data to identify multiple immunoregulatory cells after AHST that contribute to the control of reconstituting alloreactive T cells. This approach identified a loss of NK cell-mediated control of alloreactive CD4 Tem cells as the dominant immune process preceding the development of aGvHD early post-transplant. Importantly, we show that specific immunoregulatory subsets are dominant at different time-points, with increasing influence of DP T cells and CD4 Treg at later time points. Our data provide mechanistic insight into the dynamic pattern of control of alloreactivity over time and show that strategies to expand or potentiate immunoregulatory cells to prevent aGvHD should be time-dependent. Disclosures Gribben: Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding.

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

Recent technological advances in the fields of biology and medicine allow measuring single cells into unprecedented depth. This results in new types of high-throughput datasets that shed new lights on cell development, both in healthy as well as diseased tissues. However, studying these biological processes into greater detail crucially depends on novel computational techniques that efficiently mine single cell data sets. In this paper, we introduce machine learning techniques for single cell data analysis: we summarize the main developments in the field, and highlight a number of interesting new avenues that will likely stimulate the design of new types of machine learning algorithms.

Response to Orlova et al . “Science not art: statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets”

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.

© Springer International Publishing Switzerland 2015. Complex aggregates have been proposed as a way to bridge the gap between approaches that handle sets by imposing conditions on specific elements, and approaches that handle them by imposing conditions on aggregated values. A complex aggregate summarises a subset of the elements in a set, where this subset is defined by conditions on the attribute values. In this paper, we present a new type of complex aggregate, where this subset is defined to be a cluster of the set. This is useful if subsets that are relevant for the task at hand are difficult to describe in terms of attribute conditions. This work is motivated from the analysis of flow cytometry data, where the sets are cells, and the subsets are cell populations. We describe two approaches to aggregate over clusters on an abstract level, and validate one of them empirically, motivating future research in this direction. ispartof: pages:181-193 ispartof: Lecture Notes in Computer Science vol:9046 pages:181-193 ispartof: Inductive Logic Programming location:Nancy, France date:14 Sep - 16 Sep 2014 status: published

Advances in flow cytometry bioinformatics have resulted in a wide variety of clustering, classification and visualization techniques. To objectively evaluate the performance of such methods, common benchmarks such as the FlowCAP initiative have proven to be of great value. In this work, we report on a novel method, FloReMi, which was developed to tackle the most recent FlowCAP IV challenge. This challenge was formulated as a survival modeling problem, where participants were expected to design a model to predict the time until progression to AIDS for HIV patients. It is known that variability in progression rate cannot be fully predicted by simple CD4(+) T cell counts. However, it is hypothesized that the immunopathogenesis established early in HIV already indicates the course of future disease. Adequately estimating the progression rate of HIV patients is crucial in their treatment. Using an automated pipeline to preprocess the data, and subsequently identify and select informative cell subsets, a survival regression method based on random survival forests was built, which obtained the best results of all submitted approaches to the FlowCAP IV challenge. ispartof: Cytometry A vol:89 issue:1 pages:22-29 ispartof: location:United States status: published

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 Cytometry