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Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs, enabling deep spatial characterization of both healthy and diseased tissues. Parameters for the design of optimal multiplex imaging studies, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a statistical framework that determines the number and area of fields of view necessary to accurately identify all cell phenotypes that are part of a tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design. This strategy will enable an improved design of multiplexed imaging studies., Nature Methods, 20 (418), ISSN:1548-7105, ISSN:1548-7091

The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry™ (IMC™) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.

ObjectiveTo investigate the immune cell profiling and their longitudinal changes in systemic lupus erythematosus (SLE).MethodsWe employed mass cytometry with three different 38-39 marker panels (Immunophenotyping, T cell/monocyte, and B cell) in cryopreserved peripheral blood mononuclear cells (PBMCs) from nine patients with early SLE, 15 patients with established SLE, and 14 non-inflammatory controls. We used machine learning-driven clustering, FlowSOM (Flow Self-Organizing Maps) and dimensional reduction with tSNE (t-distributed Stochastic Neighbor Embedding) to identify unique cell populations in early and established SLE. For the nine early SLE patients, longitudinal mass cytometry analysis was applied to PBMCs at three time points (at enrollment, six months post-enrollment, and one year post-enrollment). Serum samples were also assayed for 65 cytokines by Luminex multiplex assay, and associations between cell types and cytokines/chemokines assessed.ResultsT peripheral helper cells (Tph cells), T follicular helper cells (Tfh cells) and several Ki67+ proliferating subsets (ICOS+ Ki67+ CD8 T cells, Ki67+ regulatory T cells, CD19int Ki67hi plasmablasts, and Ki67hi PU.1hi monocytes) were increased in early SLE. Longitudinal mass cytometry and multiplex serum cytokine assays of samples from early SLE patients revealed that Tfh cells and CXCL10 decreased at one year post-enrollment. CXCL13 correlated positively with several of the expanded cell populations in early SLE.ConclusionsTwo major helper T cell subsets and unique Ki67+ proliferating immune cell subsets were expanded in the early phase of SLE, and the immunologic features characteristic of early SLE evolved over time.

Tuberculosis (TB) is a difficult-to-treat infection because of multidrug regimen requirements based on drug susceptibility profiles and treatment observance issues. TB cure is defined by mycobacterial sterilization, technically complex to systematically assess. We hypothesized that microbiological outcome was associated with stage-specific immune changes in peripheral whole blood during TB treatment. The T-cell phenotypes of treated TB patients were prospectively characterized in a blinded fashion using mass cytometry after Mycobacterium tuberculosis (Mtb) antigen stimulation, and then correlated to sputum culture status. At two months of treatment, cytotoxic and terminally differentiated CD8+ T-cells were under-represented and naïve CD4+ T-cells were over-represented in positive-versus negative-sputum culture patients, regardless of Mtb drug susceptibility. At treatment completion, an antigen-driven T-cell immune shift towards differentiated subpopulations was associated with TB cure. Overall, we identified specific T-cell profiles associated with slow sputum converters, which brings new insights in TB prognostic biomarker research designed for clinical application.SummaryIn patients treated for pulmonary TB, high-dimensional immune profiling with mass cytometry revealed that Mycobacterium tuberculosis culture conversion is associated with newly characterized peripheral CD8+ T-cell phenotypes. This paves the way for new immune biomarkers associated with mycobacterial sterilization.

It is believed that immune responses are different between individuals and at different times. In addition, personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the cellular and molecular system mechanisms underlying such heterogeneity remain largely elusive. In this study, we conducted a systematic time-lapse single-cell analysis, using 171 single-cell libraries and 30 mass cytometry datasets intensively for seven healthy individuals. We found substantial diversity in immune cell populations and their gene expression patterns between different individuals. These patterns showed daily fluctuations even within the same individual spending a usual life. Similar diversities were also observed for the T cell receptor and B cell receptor repertoires. Detailed immune cell profiles at healthy statuses should give an essential background information to understand their immune responses, when the individual is exposed to various environmental conditions. To demonstrate this idea, we conducted the similar analysis for the same individuals on the vaccination of Influenza and SARS-CoV-2, since the date and the dose of the antigens are well-defined in these cases. In fact, we found that the distinct responses to vaccines between individuals, althougth key responses are common. Single cell immune cell profile data should make fundamental data resource to understand variable immune responses, which are unique to each individual.

Mitochondria contain their own genome which encodes 13 essential mitochondrial proteins and accumulates somatic variants at up to 10 times the rate of the nuclear genome. These mitochondrial genome variants lead to respiratory chain deficiency and cellular dysfunction. Work with the PolgAmut/PolgAmut mouse model, which has a high mitochondrial DNA mutation rate, showed enhanced levels of age related osteoporosis in affected mice along with respiratory chain deficiency in osteoblasts. To explore whether respiratory chain deficiency is also seen in human osteoblasts with age, we developed a protocol and analysis framework for imaging mass cytometry (IMC) in bone tissue sections to analyse osteoblasts in situ. We have demonstrated significant increases in complex I deficiency with age in human osteoblasts. This work is consistent with findings from the PolgAmut/PolgAmut mouse model and suggests that respiratory chain deficiency, as a consequence of the accumulation of age related mitochondrial DNA mutations, may have a significant role to play in the pathogenesis of human age related osteoporosis. O_TBL View this table: org.highwire.dtl.DTLVardef@18a0c9forg.highwire.dtl.DTLVardef@42a3b7org.highwire.dtl.DTLVardef@10a8423org.highwire.dtl.DTLVardef@1693b4borg.highwire.dtl.DTLVardef@1b3e2ae_HPS_FORMAT_FIGEXP M_TBL C_TBL

Imaging Mass Cytometry (IMC) has become a useful tool in biomedical research due to its capability to measure over 100 markers simultaneously. Unfortunately, some protein channels in IMC images can be very noisy, which may significantly affect the phenotyping results without proper data processing. We developed IMCellXMBD1, a highly effective and generalizable cell identification and quantification method for IMC images. IMCell performs denoising by subtracting an estimated background noise value from pixel values for each individual protein channel, identifies positive cells from negative cells by comparing the distribution between segmented cells and decoy cells, and normalize the protein expression levels of the identified positive cells for downstream data analysis. Experimental results demonstrate that our method significantly improves the reliability of cell phenotyping which is essential for using IMC in biomedical studies.

We propose a novel pipeline for the analysis of imaging mass cytometry data, comparing an unbiased approach, representing the actual gold standard, with a novel biased method. We made use of both synthetic/ controlled datasets as well as two datasets obtained from FFPE sections of follicular lymphoma, and head and neck patients, stained with a 14 and 29-markers panels respectively. The novel pipeline, denominated RUNIMC, has been completely developed in R and contained in a single package. The novelty resides in the ease with which multi-class random forest classifier can be used to classify image features, making the pathologist’s and expert classification pivotal, and the use of a random forest regression approach that permits a better detection of cell boundaries, and alleviates the necessity of relying on a perfect nuclear staining.

SummaryHuman cytomegalovirus (HCMV) reactivation is a major opportunistic infection after allogeneic haematopoietic stem cell transplantation and has a complex relationship with post-transplant immune reconstitution. Here, we used mass cytometry to comprehensively define global patterns of innate and adaptive immune cell reconstitution at key phases of HCMV reactivation (before detection, initial detection, peak and near resolution) in the first 100 days post-transplant. In addition to identifying patterns of immune reconstitution in those with or without HCMV reactivation, we found mucosal-associated invariant T (MAIT) cell levels at the initial detection of HCMV DNAemia distinguished patients who subsequently developed low-level versus high-level HCMV reactivation. In addition, early recovery of effector-memory CD4+ T cells distinguished low-level and high-level reactivation. Our data describe distinct immune signatures that emerged with HCMV reactivation post-HSCT, and highlight MAIT cell levels at the initial detection of reactivation as a potential prognostic marker to guide clinical decisions regarding pre-emptive therapy.

SUMMARYRNA-based vaccines against SARS-CoV-2 are critical to limiting COVID-19 severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. We used single-cell technologies to identify and characterized antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 in longitudinal samples from a cohort of healthy donors. Mass cytometry and machine learning pinpointed a novel expanding, population of antigen-specific non-canonical memory CD4+ and CD8+ T cells. B cell sequencing suggested progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlated with eventual SARS-CoV-2 IgG and a donor lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms reveal an antigen-specific cellular basis of RNA vaccine-based immunity.ONE SENTENCE SUMMARYSingle-cell profiling reveals the cellular basis of the antigen-specific response to the BNT162b2 SARS-CoV-2 RNA vaccine.

SummaryThe liver is exemplar to study tissue regeneration due to its inherent ability of repair and regrowth. It replaces its lost or injured tissue by the proliferation, interaction and temporal coordination of multiple residential cell types. Until now we lacked a detailed description of the specific contributions of each cell type to the regenerative process, and therefore analyzed mouse livers 0, 3, 6, and 24 hours following two-thirds partial hepatectomy (PHx) by single cell RNA-sequencing (scRNA-seq) and mass cytometry. Our resulting genome wide temporal atlas contains the time dependent transcriptional changes in hepatocytes, endothelial cells, bone marrow-derived macrophages (BMDM) and Kupffer cells. In addition, it describes the cell specific contribution of mitogenic growth factors from biliary epithelial, endothelial and stellate cells as well as chemokines and cytokines from BMDM and granulocytes. And interestingly, Kupffer cells as opposed to hepatocytes emerged as the first cell to proliferate presenting a new dynamic in the liver following PHx. Here, we provide a robust data set at cellular resolution to uncover new elements and revisit current dogmas on the mechanisms underlying liver regeneration. To facilitate access to the data, we have launched the portal www.phxatlas.ch in which the scRNA-seq data can be visualized.

Although the formation of a durable neutralizing antibody response after an acute viral infection is a key component of protective immunity, little is known about why some individuals generate high versus low neutralizing antibody titers to infection or vaccination. Infection with Zika virus (ZIKV) during pregnancy can cause devastating fetal outcomes, and efforts to understand natural immunity to this infection are essential for optimizing vaccine design. In this study, we leveraged the high-dimensional single-cell profiling capacity of mass cytometry (CyTOF) to deeply characterize the cellular immune response to acute and convalescent ZIKV infection in a cohort of blood donors in Puerto Rico incidentally found to be viremic during the 2015-2016 epidemic in the Americas. During acute ZIKV infection, we identified widely coordinated responses across innate and adaptive immune cell lineages. High frequencies of multiple activated innate immune subsets, as well as activated follicular helper CD4+ T cells and proliferating CD27-IgD-B cells, during acute infection were associated with high titers of ZIKV neutralizing antibodies at 6 months post-infection. On the other hand, low titers of ZIKV neutralizing antibodies were associated with immune features that suggested a cytotoxic-skewed immune “set-point.” Our study offers insight into the cellular coordination of immune responses and identifies candidate cellular biomarkers that may offer predictive value in vaccine efficacy trials for ZIKV and other acute viral infections aimed at inducing high titers of neutralizing antibodies.One Sentence SummaryMass cytometry reveals acute ZIKV infection cellular immune signatures that predict high or low neutralizing antibody titers 6 months post-infection.

SUMMARYType 2 diabetes mellitus (T2D) is a chronic age-related disorder characterized by hyperglycemia due to the failure of pancreatic beta cells to compensate for increased insulin demand, typically associated with peripheral insulin resistance. However, despite decades of research, the pathogenic mechanisms underlying T2D remain poorly defined. Imaging mass cytometry (IMC) enables multiplexed assessment of the abundance and subcellular localization of multiple proteins on the same tissue section. Herein, we utilized IMC with a panel of 34 antibodies to quantify key markers of pancreatic exocrine, islet, and immune cells as well as stromal components. We employed this panel to analyze over 2.1 million cells from 16 pancreata obtained from donors with T2D and 13 pancreata from age similar non-diabetic controls. In the T2D pancreata, we observed significant alterations in islet architecture, endocrine cell composition, and surprisingly immune cell constituents. Thus, both HLA-DR positive CD8 T cells and macrophages were enriched intra-islet in the T2D pancreas. These efforts demonstrate the utility of IMC to investigate complex events at the cellular level in order to provide new insights into the pathophysiology of T2D.

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. Inhibition and elimination of EMT-undergoing cells are therefore promising therapy approaches. However, detecting EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. Here, we profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a FACS surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by chronic exposure to TGFβ1 or 4-hydroxytamoxifen, respectively. We observed a spectrum of EMT transition phenotypes in each cell line and the spectrum varied across the time course. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used herein, and for the design of EMT time course experiments.Mendeley Data: DOI: 10.17632/pt3gmyk5r2.1ArrayExpress Data: Accession number E-MTAB-9365

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.Graphical AbstractKey pointsOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics dataPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76Top models of whole signaling response models perform almost as well as a biological replicateCell-line specific variation in dynamics can be predicted from basal omics

COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. To expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients, including matched analysis of the whole-blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate herein the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide., Graphical Abstract, Sullivan et al. describe the development of a multidimensional dataset for the study of COVID-19 known as the COVIDome, which includes diverse clinical, transcriptome, proteome, metabolome, and immune cell datasets. A researcher portal known as the COVIDome Explorer was created to enable global data access and analysis.

BackgroundRadiation-induced cardiac injury results in complex clinical presentations, unique management issues, and increased morbidity and mortality. But the underlying mechanism of radiation-induced cardiac injury is still unclear. The aim of this study is to explain the compositional alterations of innate and adaptive cells after thoracic irradiation and the relationship of macrophage with other adaptive cells.MethodsC57BL/6 mice were anesthetized and performed with 20Gy single dose cardiac irradiation. We carried out mice echocardiography to examine cardiac function. Masson and immunohistochemical staining were adopted to analyze cardiac fibrosis. Blood and cardiac tissue were collected 7 days, 4 weeks and 8 weeks after irradiation and sham-irradiated mice were set as a control group. Part of blood and tissue samples were analyzed with mass cytometry. We also used remaining blood samples to measure pro-fibrotic cytokines.ResultsWe observed reduced cardiac diastolic function and pathologically confirmed cardiac fibrosis after thoracic irradiation. Through mass cytometry, we identified that the proportion of neutrophils, macrophages and monocytes elevated. The ratio of T and B cells decreased after irradiation in the blood samples. For tissue samples, the proportion of macrophages, monocytes and neutrophils increased, endothelial cells reduced, but T and B cells also increased. The level of TGF-β, TNF-α, IL-6 climbed considerably, all of them are closely associated with the evolution of macrophage function during recovering phase of cardiac injury. In the correlation plot, we also discovered that CD4+T and CD8+T cells were strongly positively correlated with macrophages.ConclusionWe illustrated the immunological response after radiation-induced cardiac fibrosis, and macrophage may play a crucial role during the process. The results might have therapeutic potential for targeting macrophages in order to reduce radiation-induced cardiac fibrosis.

Key immune signatures of SARS-CoV-2 infection may associate with either adverse immune reactions (severity) or simply an ongoing anti-viral response (temporality); how immune signatures contribute to severe manifestations and/or temporal progression of disease and whether longer disease duration correlates with severity remain unknown. Patient blood was comprehensively immunophenotyped via mass cytometry and multiplex cytokine arrays, leading to the identification of 327 basic subsets that were further stratified into more than 5000 immunotypes and correlated with 28 plasma cytokines. Low-density neutrophil abundance was closely correlated with hepatocyte growth factor levels, which in turn correlated with disease severity. Deep analysis also revealed additional players, namely conventional type 2 dendritic cells, natural killer T cells, plasmablasts and CD16+ monocytes, that can influence COVID-19 severity independent of temporal progression. Herein, we provide interactive network analysis and data visualization tools to facilitate data mining and hypothesis generation for elucidating COVID-19 pathogenesis.

Multiplexed immune cell profiling of the tumor microenvironment (TME) in cancer has improved our understanding of cancer immunology, but complex spatial analyses of tumor-immune interactions in lymphoma are lacking. Here we used imaging mass cytometry (IMC) on 33 cases of diffuse large B cell lymphoma (DLBCL) to characterize tumor and immune cell architecture and correlate it to clinicopathological features such as cell of origin, gene mutations, and responsiveness to chemotherapy. To understand the poor response of DLBCL to immune check point inhibitors (ICI), we compared our results to IMC data from Hodgkin lymphoma (HL), a cancer highly responsive to ICI, and observed differences in the expression of PD-L1, PD-1, and TIM-3. We created a spatial classification of tumor cells and identified sub-regions of immune activation, immune suppression, and immune exclusion within the topology of DLBCL. Finally, the spatial analysis allowed us to identify markers such as CXCR3, which are associated with penetration of immune cells into immune desert regions, with important implications for engineered cellular therapies.SIGNIFICANCEThis is the first study to integrate tumor mutational profiling, cell of origin classification, and multiplexed immuno-phenotyping of the TME into a spatial analysis of DLBCL at the single cell level. We demonstrate that, far from being histo-pathologically monotonous, DLBCL has a complex tumor architecture, and that changes in tumor topology can be correlated with clinically relevant features. This analysis identifies candidate biomarkers and therapeutic targets such as TIM-3, CCR4, and CXCR3 that are relevant for combination treatment strategies in immuno-oncology and cellular therapies such as CAR-T cells.

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

In this study, we leveraged a combination of single cell RNAseq, cytometry by time of flight (CyTOF), and flow cytometry to study the biology of a unique macrophage population in pulmonary fibrosis. Using the profiling data from 312,928 cells derived from 32 idiopathic pulmonary fibrosis (IPF), 29 healthy control and 18 chronic obstructive pulmonary disease (COPD) lungs, we identified an expanded population of macrophages in IPF that have a unique transcriptional profile associated with pro-fibrotic signature. These macrophages attain a hybrid transitional state between alveolar and interstitial macrophages, are enriched with biological processes of pro-fibrotic immune cells, and express novel surface markers and genes that have not been previously reported. We then applied single cell CyTOF to simultaneously measure 37 markers to precisely phenotype the uniquely expanded macrophage subset in IPF lungs. The SPADE algorithm independently identified an expanded macrophage cluster, and validated CD84 and CD36 as novel surface markers that highly label this cluster. Using a separate validation cohort, we confirmed an increase in CD84++CD36++ macrophage population in IPF compared to control and COPD lungs by flow cytometry. Further, using the signature from the IPF-specific macrophages and the LINCS drug database, we predicted small molecules that could reverse the signature of IPF-specific macrophages, and validated two molecules, CRT and Cucur, using THP-1 derived human macrophages and precision-cut lung slices (PCLS) from IPF patients. Utilizing a multi-dimensional translational approach, our work identified a novel and targetable population of macrophages found in end-stage pulmonary fibrosis.One Sentence SummarySingle cell RNAseq, CyTOF, and flow cytometry reveal the presence of an aberrant macrophage population in pulmonary fibrosis

Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. Results Differential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the R package and workflow for both strategies on a pregnancy dataset. Conclusion Our approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.

High-dimensional mass cytometry (CyTOF) phenotyping allows for the routine measurement of over 40 parameters and is increasingly being utilized across a wide range of studies. However, CyTOF-specific panel design and optimization represent challenges to wider adoption and standardization of immune profiling with CyTOF. To address this, Fluidigm recently commercialized its MaxPar Direct Immune Profiling Assay (MDIPA), which comprises a lyophilized 30-marker antibody panel that is able to identify all major circulating immune cell subsets and offers a streamlined solution for standardized human immune monitoring. However, in the course of applying the MDIPA to characterize large numbers of whole blood samples, we observed several instances of unusual aberrant staining patterns, most notably CD19 expression on non-B cells, which can potentially confound data analysis and lead to erroneous interpretation of results when using this assay. Here, we report that this complex phenomenon is mediated by donor-specific plasma factors that mediate non-specific interactions between specific antibodies in the MDIPA panel. Our findings additionally suggest specific strategies that can be used to mitigate the issue, including the use of PBMCs or lysed/washed whole blood to remove endogenous plasma prior to staining, or blocking specific antibodies in the MDIPA panel.

Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin-fixed, paraffin-embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal-to-noise ratio can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. We investigated the effect of immunodetection-related signal intensity fluctuations on IMC analysis and phenotype identification in a cohort of twelve colorectal cancer tissues. Furthermore, we explored different normalisation strategies and propose a workflow to normalise IMC data by semi-automated background removal, using publicly available tools. This workflow can be directly applied to previously obtained datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.

SummaryRecent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19)1–8. However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.

As the size and complexity of high-dimensional cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large high-dimensional datasets, generated by flow cytometry, mass cytometry (CyTOF), or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing (scRNAseq) or high-dimensional imaging technologies, such as Imaging Mass Cytometry (IMC). The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

Obesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine if tumors evolving in obese environments adapt differential interactions with immune cells and if this can be connected to disease outcome. Most of these studies have been limited to single cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T cell suppressive cell types and a decrease in CD8 T-cells.

Acute respiratory distress syndrome (ARDS) is the main complication of COVID-19, requiring admission to Intensive Care Unit (ICU). Despite recent immune profiling of COVID-19 patients, to what extent COVID-19-associated ARDS specifically differs from other causes of ARDS remains unknown, To address this question, we built 3 cohorts of patients categorized in COVID-19negARDSpos, COVID-19posARDSpos, and COVID-19posARDSneg, and compared their immune landscape analyzed by high-dimensional mass cytometry on peripheral blood followed by artificial intelligence analysis. A cell signature associating S100A9/calprotectin-producing CD169pos monocytes, plasmablasts, and Th1 cells was specifically found in COVID-19posARDSpos, unlike COVID-19negARDSpos patients. Moreover, this signature was shared by COVID-19posARDSneg patients, suggesting severe COVID-19 patients, whatever they experienced or not ARDS, displayed similar immune dysfunctions. We also showed an increase in CD14posHLA-DRlow and CD14lowCD16pos monocytes correlated to the occurrence of adverse events during ICU stay. Our study demonstrates that COVID-19-associated ARDS display a specific immune profile, and might benefit from personalized therapy in addition to standard ARDS management.One Sentence SummaryCOVID-19-associated ARDS is biologically distinct from other causes of ARDS.

Cryopreserved peripheral blood mononuclear cells (PBMCs) are frequently collected and provide disease- and treatment-relevant data in clinical studies. Here, we developed combined protein (40 antibodies) and transcript single cell (sc)RNA sequencing in PBMCs. Among 31 participants in the WIHS Study, we sequenced 41,611 cells. Using Boolean gating followed by Seurat UMAPs and Louvain clustering, we identified 58 subsets among CD4 T, CD8 T, B, NK cells and monocytes. This resolution was superior to flow cytometry, mass cytometry or scRNA-sequencing without antibodies. Since the transcriptome was not needed for cell identification, combined protein and transcript scRNA-Seq allowed for the assessment of disease-related changes in transcriptomes and cell type proportion. As a proof-of-concept, we showed such differences between healthy and matched individuals living with HIV with and without cardiovascular disease. In conclusion, combined protein and transcript scRNA sequencing is a suitable and powerful method for clinical investigations using PBMCs.

High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of multiparametric “big data” usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: 1, data import and quality control, 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework.

SUMMARYHighly multiplexed imaging technologies enable spatial profiling of dozens of biomarkersin situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describecytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Usingcytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility ofcytomapperby analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition,cytomapperincludes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together,cytomapperoffers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

Diabetic patients taking metformin have lower risk for Mycobacterium tuberculosis (Mtb) infection, progression from infection to tuberculosis (TB) disease, TB morality and TB recurrence. However, a detailed mechanistic understanding of metformin’s protective immunological benefits on host resistance to TB is lacking. In this study, using mass cytometry we show that metformin treatment expands memory-like antigen-inexperienced CD8+CXCR3+ T cells in naïve mice, and in healthy and diabetic humans. Metformin-educated CD8+ T cells have increased (i) mitochondrial mass, oxidative phosphorylation, and fatty acid oxidation; (ii) survival capacity; and (iii) anti-mycobacterial properties. CD8+ T cells from CXCR3−/− mice did not exhibit metformin-mediated metabolic programming. In BCG-vaccinated mice and guinea pigs, metformin enhanced immunogenicity and protective efficacy against Mtb challenge. Collectively, our results demonstrate an important role of CD8+ T cells in metformin-derived host metabolic-fitness towards Mtb infection.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead ‘left out’ to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

Dengue virus (DENV) is a significant cause of morbidity in many regions of the world, with children at the greatest risk of developing severe dengue. Natural killer (NK) cells, characterized by their ability to rapidly recognize and kill virally infected cells, are activated during acute DENV infection. However, their role in viral clearance versus pathogenesis has not been fully elucidated. Our goal was to profile the NK cell receptor-ligand repertoire to provide further insight into the function of NK cells during pediatric and adult DENV infection. We used mass cytometry (CyTOF) to phenotype isolated NK cells and peripheral blood mononuclear cells (PBMCs) from a cohort of DENV-infected children and adults. Using unsupervised clustering, we found that pediatric DENV infection leads to a decrease in total NK cell frequency with a reduction in the percentage of CD56dimCD38bright NK cells and an increase in the percentage of CD56dimperforinbright NK cells. No such changes were observed in adults. Next, we identified markers predictive of DENV infection using a differential state test. In adults, NK cell expression of activation markers, including CD69, perforin, and Fas-L, and myeloid cell expression of activating NK cell ligands, namely Fas, were predictive of infection. In contrast, NK cell expression of the maturation marker CD57 and increased myeloid cell expression of inhibitory ligands, such as HLA class I molecules, were predictive of pediatric DENV infection. These findings suggest that acute pediatric DENV infection may result in diminished NK cell activation, which could contribute to enhanced pathogenesis and disease severity.

Single-cell methods such as flow cytometry, Mass cytometry and single-cell mRNA sequencing collect high-dimensional data on thousands to millions of individual cells. An important aim during the analysis of such data is to classify cells into known categories and cell types. One commonly used approach towards this is clustering of cells with similar features followed by manual annotation of clusters in relation to known biology. A second approach, commonly used for cytometry data relies on manual sorting or “gating” of cells, often based on pairwise combinations of measurements used in a stepwise and very tedious process of cell annotation. Both of these approaches require manual inspection and annotation of every new dataset generated, a process that is not only time consuming but also subjective and surely influential for the conclusions drawn. The manual annotation is also difficult to reproduce by other researchers with a different perception of features that signify their cells of interest. Here we propose an alternative strategy based on machine learning of known phenotypes from manually curated, high-dimensional data and thereby enabling rapid classification of subsequent datasets in a more reproducible manner. This simple approach increases both throughput, reproducibility and simplicity of cell classification in single-cell biology.

Tumor-specific T cells likely underpin effective immune checkpoint-blockade therapies. Yet, most studies focus on Treg cells and CD8+tumor-infiltrating lymphocytes (TILs). Here we study CD4+TILs in human lung and colorectal cancers and observe that non-Treg CD4+TILs average more than 70% of total CD4+TILs in both cancer types. Leveraging high dimensional analyses including mass cytometry and single-cell sequencing, we reveal that CD4+TILs are heterogeneous at both gene and protein levels, within each tumor and across patients. Consistently, we find different subsets of CD4+TILs showing characteristics of effectors, tissue resident memory (Trm) or exhausted cells (expressing PD-1, CTLA-4 and CD39). In both cancer types, the frequencies of CD39−non-Treg CD4+TILs strongly correlate with frequencies of CD39−CD8+TILs, which we and others have previously shown to be enriched for cells specific for cancer-unrelated antigens (bystanders).Ex-vivo, we demonstrate that CD39−CD4+TILs can be specific for cancer unrelated antigens, such as HCMV epitopes. Overall, our findings highlight that CD4+TILs cells are not necessarily tumor-specific and suggest measuring CD39 expression as a straightforward way to quantify or isolate bystander CD4+T cells.Graphical abstract

Sleeve gastrectomy (SG) has profound, immediate weight-loss independent effects on obesity related diabetes (T2D). Our prior studies have shown that immunologic remodeling may play a part in this metabolic improvement. However, to date, little is known about how the major immune cell populations change following SG. Using mass cytometry with time of flight analysis (CyTOF) we aimed to broadly explore the organ-specific immune cell repertoire induced by SG. Surgery was performed on obese, insulin resistant and lean mice in order to understand surgery-specific phenotypes. We identified a shift within the splenic B cell compartment with a reduction in follicular and an increase in innate-like B cell subsets in SG animals. There was a concomitant increase in multiple circulating immunoglobulin classes. Further, SG animals had a conserved increase in splenic neutrophils and a tendency toward M2 macrophage polarization. Others have shown that these, weight-loss independent, surgery-specific changes are linked to improved glucose metabolism and thus, may be a major contributor to post SG physiology. Characterizing the complex immune milieu following SG is an important step toward understanding the physiology of SG and the potential therapies therein.

High grade non-muscle-invasive bladder tumours are treated with transurethral resection followed by recurrent intravesical instillations of Bacillus Calmette Guérin (BCG). Although bladder cancer patients respond well to BCG, important questions remain unanswered, including how to identify at early stages non-responder patients and patients at risk to abandon the treatment. Here, we analysed the cells released into the urine of bladder cancer patients longitudinally 3-7 days after BCG instillations. Mass cytometry (CyTOF) analyses revealed that most cells were granulocytes and monocytes rather than effector lymphocytes, and most expressed activation markers. A novel population of CD15+CD66b+CD14+ CD16+ cells was very abundant in several samples and expression of these markers was confirmed using flow cytometry and qPCR. Samples of patients with a stronger inflammatory response contained more cells in urine; however, this was not due to haematuria, as the proportions of the cell populations observed were different from blood. We provide the proof-of-concept for a new approach to analyse samples that may help classify patients and identify those at risk of BCG infection and other unwanted BCG-related events.

Simultaneous measurement of surface proteins and gene expression within single cells offers high resolution snapshots of complex cell populations. These methods rely on staining cells with oligo-conjugated antibodies analogous to staining for flow- and mass cytometry. Unlike flow- and mass cytometry, signal from oligo-conjugated antibodies is not hampered by spectral overlap or limited by the number of metal isotopes, making it a highly sensitive and scalable approach. Signal from oligo-conjugated antibodies is quantified by counting reads from high-throughput sequencing. Consequently, cost of sequencing is strictly dependent on the signal intensities and background from the pool of antibodies used in analysis. Thus, considering the "cost-of-signal" as well as optimizing "signal-to-noise", makes titration of oligo-conjugated antibody panels more complex and even more important than for flow- and mass cytometry. In this study, we investigated the titration response of a panel of oligo-conjugated antibodies towards four variables: Antibody concentration, staining volume, cell number at staining, and tissue of origin. We find that staining with high antibody concentrations recommended by published protocols and commercial vendors cause unnecessarily high background signal and that concentrations of many antibodies can be drastically reduced without loss of biological information. Reducing staining volume only affects antibodies targeting highly abundant epitopes used at low concentrations and can be counteracted by reducing cell numbers at staining. We find that background signal from empty droplets can account for a major fraction of the total sequencing reads and is primarily derived from antibodies used at high concentrations. Together, this study provides new insight into the titration response and background signal of oligo-conjugated antibodies and offers concrete guidelines on how such panels can be improved.

Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.

Single cell mass cytometry (SCMC) combines features of traditional flow cytometry (FACS) with mass spectrometry and allows the measurement of several parameters at the single cell level, thus permitting a complex analysis of biological regulatory mechanisms. We optimized this platform to analyze the cellular elements, the hemocytes, of theDrosophilainnate immune system. We have metal-conjugated six antibodies against cell surface antigens (H2, H3, H18, L1, L4, P1), against two intracellular antigens (3A5, L2) and one anti-IgM for the detection of L6 surface antigen, as well as one anti-GFP for the detection of crystal cells in the immune induced samples. We investigated the antigen expression profile of single cells and hemocyte populations in naive, in immune induced states, in tumorous mutants (hopTumbearing a driver mutation andl(3)mbn1carrying deficiency of a tumor suppressor) as well as in stem cell maintenance defectivehdcΔ84mutant larvae. Multidimensional analysis enabled the discrimination of the functionally different major hemocyte subsets, lamellocytes, plasmatocytes, crystal cell, and delineated the unique immunophenotype of the mutants. We have identified sub-populations of L2+/P1+ (l(3)mbn1), L2+/L4+/P1+ (hopTum)transitional phenotype cells in the tumorous strains and a sub-population of L4+/P1+ cells upon immune induction. Our results demonstrated for the first time, that mass cytometry, a recent single cell technology combined with multidimensional bioinformatic analysis represents a versatile and powerful tool to deeply analyze at protein level the regulation of cell mediated immunity ofDrosophila.

BackgroundTumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including notably immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun to use single cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in bench to bedside research.MethodWe previously developed a computational transfer learning approach to identify shared biology between independent high-throughput single-cell RNA sequencing (scRNA-seq) datasets. In the present study, we test this framework’s ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and further expand its application beyond comparison of scRNA-seq datasets into comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry.ResultsWe found a conserved signature of NK cell activation in anti-CTLA-4 responsive mice and human tumors. In human melanoma, we found that the NK cell activation signature correlates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 independent of the antibody binding receptor (FcR), and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation.ConclusionsThese data demonstrate the ability of our transfer learning approach to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many immuno-oncology questions, enhancing bench to bedside research and enabling better understanding and treatment of disease.Graphical Abstract

MotivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single cell resolution. These large amounts of data, however, require dedicated, interactive tools for translating the data into knowledge.ResultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automatizes the use of of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood as well as cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples.AvailabilityThe method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cytoContactsampsa.hautaniemi@helsinki.fiSupplementary informationSupplementary material is available and FCS files are hosted at flowrepository.org/id/FR-FCM-Z2LW

A holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.

Mass cytometry (CyTOF) is a technology that has revolutionised single cell biology. One illuminating application of CyTOF has been in understanding the mechanisms of blood cancer resistance to therapy. Longitudinal studies of clinical cohorts during drug treatment provide a deeper understanding of the molecular changes that underlie sensitivity or resistance to treatment in each patient. However, understanding the biological impact of a cancer drug in such studies necessitates the integration of multiple CyTOF batches. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches which includes an R-Shiny application with diagnostics plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/., Author summary Cytometry is a popular technology used to quantify biological material. In recent years, the capabilities of cytometry have expanded, resulting in ever larger datasets. In order to analyse these data, new approaches are required, giving rise to the field of cytometry bioinformatics. Despite the success of numerous algorithms and tools in this domain, widespread adoption by the scientific community has yet to be realised. Here we introduce CytoPy, a comprehensive cytometry data analysis framework deployed in Python, a beginner-friendly programming language. We validate CytoPy’s ability to handle batch effects and identify immune cell populations in human blood. Subsequently, we apply CytoPy to the analysis of drain fluid from patients undergoing peritoneal dialysis and compare the local immune response of stable patients to those presenting with acute peritonitis. CytoPy is open-source and available online: https://cytopy.readthedocs.io/en/latest/.

Glioblastoma, the most aggressive brain cancer, often recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. Here, we show that patient-derived GSCs, but not normal astrocytes, are highly sensitive to lysis by healthy allogeneic natural killer (NK) cellsin vitro. In contrast, single cell analysis of autologous, tissue infiltrating NK cells isolated from surgical samples of high-grade glioblastoma patient tumors using mass cytometry and single cell RNA sequencing revealed an abnormal phenotype associated with impaired lytic function compared with peripheral blood NK cells from GBM patients or healthy donors. This immunosuppression was attributed to an integrin-TGF-β mechanism, activated by direct cell-cell contact between GSCs and NK cells. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-β signaling, or withTGF-β receptor 2gene-edited NK cells prevented GSC-induced NK cell dysfunction and tumor growth. Collectively, our findings reveal a novel mechanism of NK cell immune evasion by GSCs and implicate the integrin-TGF-β axis as a useful therapeutic target to eliminate GSCs in this devastating tumor.

The development of DNA-barcoded antibodies to tag cell-surface molecules has enabled the use of droplet-based single cell sequencing (dsc-seq) to profile the surface proteomes of cells. Compared to flow and mass cytometry, the major limitation of current dsc-seq-based workflows is the high cost associated with profiling each cell, thus precluding its use in applications where millions of cells are required. Here, we introduce SCITO-seq, a new workflow that combines combinatorial indexing and commercially available dsc-seq to enable cost-effective cell surface proteomic sequencing of greater than 105 cells per microfluidic reaction. We demonstrate SCITO-seq’s feasibility and scalability by profiling mixed species cell lines and mixed human T and B lymphocytes. To further demonstrate its applicability, we show comparable cellular composition estimates in peripheral blood mononuclear cells obtained with SCITO-seq and mass cytometry. SCITO-seq can be extended to include simultaneous profiling of additional modalities such as transcripts and accessible chromatin or tracking of experimental perturbations such as genome edits or extracellular stimuli.

Single-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

Cardiac non-myocytes comprise a diverse and crucial cell population in the heart that plays dynamic roles in cardiac wound healing and growth. Non-myocytes broadly fall into four cell types: endothelium, fibroblasts, leukocytes, and pericytes. Here we characterize the diversity of the non-myocytes in vivo and in vitro using mass cytometry. By leveraging single-cell RNA sequencing we inform the design of a mass cytometry panel. To aid in annotation of the mass cytometry datasets, we utilize data integration with a neural network. We introduce approximately 460,000∼ single cell proteomes of non-myocytes as well as 5,000∼ CD31 negative single cell transcriptomes. Using our data, as well as previously reported datasets, we characterize cardiac non-myocytes with high depth in six mice, characterizing novel surface markers (CD9, CD200, Notch3, and FolR2). Further, we find that extended cell culture promotes the proliferation of CD45+CD11b+FolR2+IAIE- myeloid cells in addition to fibroblasts.