Automated mapping of phenotype space with single-cell data

Detection and quantification of microplastics in various types of human tumor tissues

Microplastics (MPs) have been detected in various human tissues. However, whether MPs can accumulate within tumors and how they affect the tumor immune microenvironment (TIME) and therapeutic responses remains unclear. This study aimed to determine the presence of MPs in tumors and their potential effects on the TIME. Sixty-one tumor samples were collected for analysis. The presence of MPs in tumors was qualitatively and quantitatively assessed using pyrolysis-gas chromatography-mass spectrometry. MPs were detected in 26 of the samples examined. Three types of MPs were identified: polystyrene, polyvinyl chloride, and polyethylene. In lung, gastric, colorectal, and cervical tumors, the MP detection rates were 80 %, 40 %, 50 %, and 17 % (7.1-545.9 ng/g), respectively. MPs were detected in 70 % of pancreatic tumors (18.4-427.1 ng/g) but not detected in esophageal tumors. In pancreatic cancer, the MP-infiltrated TIME exhibited a reduction in CD8+ T, natural killer, and dendritic cell counts, accompanied by substantial neutrophil infiltration. This study illustrates the potential presence of MPs in diverse tumors; varying adhesive affinities were observed among different tumor types. MPs may lead to a more adverse TIME in pancreatic tumors. Further investigations are warranted to assess whether MPs promote tumor progression and affect the efficacy of immunotherapy.

Protocol for high-plex, whole-slide imaging of human formalin-fixed paraffin-embedded tissue using PhenoCycler-Fusion

Single-cell spatial analysis of proteins is rapidly becoming increasingly important in revealing biological insights. Here, we present a protocol for automated high-plex multi-slide immunofluorescence staining and imaging of human head and neck cancer formalin-fixed paraffin-embedded (FFPE) sections using PhenoCycler-Fusion 2.0 technology. We describe steps for preparing human head and neck cancer FFPE tissues, staining with a panel of immunophenotyping markers, and Flow Cell assembly. We then detail procedures for setting up for a PhenoCycler-Fusion run, post-run Flow Cell removal, and downstream analyses. For complete details on the use and execution of this protocol, please refer to Jhaveri et al.1.

Single-cell mass cytometry in immunological skin diseases

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.

Metronomic chemotherapy plus anti-PD-1 in metastatic breast cancer: a Bayesian adaptive randomized phase 2 trial

It remains unclear whether metronomic chemotherapy is superior to conventional chemotherapy when combined with immune checkpoint blockade. Here we performed a phase 2 clinical trial of metronomic chemotherapy combined with PD-1 blockade to compare the efficacy of combined conventional chemotherapy and PD-1 blockade using Bayesian adaptive randomization and efficacy monitoring. Eligible patients had metastatic HER2-negative breast cancer and had not received more than one prior line of standard chemotherapy. Patients (total n = 97) were randomized to receive (1) metronomic vinorelbine (NVB) monotherapy (n = 11), (2) NVB plus anti-PD-1 toripalimab (n = 7), (3) anti-angiogenic bevacizumab, NVB and toripalimab (n = 27), (4) conventional cisplatin, NVB and toripalimab (n = 26), or (5) metronomic cyclophosphamide, capecitabine, NVB and toripalimab (the VEX cohort) (n = 26). The primary endpoint was disease control rate (DCR). Secondary objectives included progression-free survival (PFS) and safety. The study met the primary endpoint. The VEX (69.7%) and cisplatin (73.7%) cohorts had the highest DCR. The median PFS of patients in the VEX cohort was the longest, reaching 6.6 months, followed by the bevacizumab (4.0 months) and cisplatin (3.5 months) cohorts. In general, the five regimens were well tolerated, with nausea and neutropenia being the most common adverse events. An exploratory mass cytometry analysis indicated that metronomic VEX chemotherapy reprograms the systemic immune response. Together, the clinical and translational data of this study indicate that metronomic VEX chemotherapy combined with PD-1 blockade can be a treatment option in patients with breast cancer. ClinicalTrials.gov Identifier: NCT04389073 .

Tracking single hiPSC-derived cardiomyocyte contractile function using CONTRAX an efficient pipeline for traction force measurement

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models to study the mechanisms underlying cardiomyopathies and cardiotoxicity. Quantification of the contractile function in single hiPSC-CMs at high-throughput and over time is essential to disentangle how cellular mechanisms affect heart function. Here, we present CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time. Three software modules enable: parameter-based identification of single hiPSC-CMs; automated video acquisition of >200 cells/hour; and contractility measurements via traction force microscopy. We analyze >4,500 hiPSC-CMs over time in the same cells under orthogonal conditions of culture media and substrate stiffnesses; +/- drug treatment; +/- cardiac mutations. Using undirected clustering, we reveal converging maturation patterns, quantifiable drug response to Mavacamten and significant deficiencies in hiPSC-CMs with disease mutations. CONTRAX empowers researchers with a potent quantitative approach to develop cardiac therapies.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-Dimensional Immune Cell Characterization

Flow cytometry is a widely used technique for immune cell analysis, offering insights into cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large antibody panels can be challenging using traditional bi-axial gating strategies. Here, we present a novel analysis pipeline designed to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 months) and aged (18-19 months) female mice then stained these with a panel of 20 fluorescently labeled antibodies. Spectral flow cytometry was performed, followed by data processing and analysis using Python within a Jupyter Notebook environment to perform batch correction, unsupervised clustering, dimensionality reduction, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 34 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with certain clusters enriched in one age group over the other. Naïve, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. Additionally, γδ T cell clusters showed differential abundance between age groups. By leveraging high-dimensional analysis methods borrowed from single-cell RNA sequencing analysis, we identified age-related differences in T cell subsets, providing insights into the immune aging process. This approach offers a robust, free, and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

Multiplex Imaging for Cell Phenotyping of Early Human Atherosclerosis

BACKGROUND

Previous studies using animal models and cultured cells suggest that vascular smooth muscle cells (SMCs) and inflammatory cytokines are important players in atherogenesis. Validating these findings in human disease is critical to designing therapeutics that target these components. Multiplex imaging is a powerful tool for characterizing cell phenotypes and microenvironments using biobanked human tissue sections. However, this technology has not been applied to human atherosclerotic lesions and needs to first be customized and validated.

METHODS AND RESULTS

For validation, we created an 8-plex imaging panel to distinguish foam cells from SMC and leukocyte origins on tissue sections of early human atherosclerotic lesions (n=9). The spatial distribution and characteristics of these foam cells were further analyzed to test the association between SMC phenotypes and inflammation. Consistent with previous reports using human lesions, multiplex imaging showed that foam cells of SMC origin outnumbered those of leukocyte origin and were enriched in the deep intima, where the lipids accumulate in early atherogenesis. This new technology also found that apoptosis or the expression of pro-inflammatory cytokines were not more associated with foam cells than with nonfoam cells in early human lesions. More CD68+ SMCs were present among SMCs that highly expressed interleukin-1β. Highly inflamed SMCs showed a trend of increased apoptosis, whereas leukocytes expressing similar levels of cytokines were enriched in regions of extracellular matrix remodeling.

CONCLUSIONS

The multiplex imaging method can be applied to biobanked human tissue sections to enable proof-of-concept studies and validate theories based on animal models and cultured cells.

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX

The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

Identification of immune subsets with distinct lectin binding signatures using multi-parameter flow cytometry: correlations with disease activity in systemic lupus erythematosus

Objectives

Cell surface glycosylation can influence protein-protein interactions with particular relevance to changes in core fucosylation and terminal sialylation. Glycans are ligands for immune regulatory lectin families like galectins (Gals) or sialic acid immunoglobulin-like lectins (Siglecs). This study delves into the glycan alterations within immune subsets of systemic lupus erythematosus (SLE).

Methods

Evaluation of binding affinities of Galectin-1, Galectin-3, Siglec-1, Aleuria aurantia lectin (AAL, recognizing core fucosylation), and Sambucus nigra agglutinin (SNA, specific for α-2,6-sialylation) was conducted on various immune subsets in peripheral blood mononuclear cells (PBMCs) from control and SLE subjects. Lectin binding was measured by multi-parameter flow cytometry in 18 manually gated subsets of T-cells, NK-cells, NKT-cells, B-cells, and monocytes in unstimulated resting state and also after 3-day activation. Stimulated pre-gated populations were subsequently clustered by FlowSOM algorithm based on lectin binding and activation markers, CD25 or HLA-DR.

Results

Elevated AAL, SNA and CD25+/CD25- SNA binding ratio in certain stimulated SLE T-cell subsets correlated with SLE Disease Activity Index 2000 (SLEDAI-2K) scores. The significantly increased frequencies of activated AALlow Siglec-1low NK metaclusters in SLE also correlated with SLEDAI-2K indices. In SLE, activated double negative NKTs displayed significantly lower core fucosylation and CD25+/CD25- Siglec-1 binding ratio, negatively correlating with disease activity. The significantly enhanced AAL binding in resting SLE plasmablasts positively correlated with SLEDAI-2K scores.

Conclusion

Alterations in the glycosylation of immune cells in SLE correlate with disease severity, which might represent potential implications in the pathogenesis of SLE.

GateMeClass: Gate Mining and Classification of cytometry data

MOTIVATION

Cytometry comprises powerful techniques for analyzing the cell heterogeneity of a biological sample by examining the expression of protein markers. These technologies impact especially the field of oncoimmunology, where cell identification is essential to analyze the tumor microenvironment. Several classification tools have been developed for the annotation of cytometry datasets, which include supervised tools that require a training set as a reference (i.e. reference-based) and semisupervised tools based on the manual definition of a marker table. The latter is closer to the traditional annotation of cytometry data based on manual gating. However, they require the manual definition of a marker table that cannot be extracted automatically in a reference-based fashion. Therefore, we are lacking methods that allow both classification approaches while maintaining the high biological interpretability given by the marker table.

RESULTS

We present a new tool called GateMeClass (Gate Mining and Classification) which overcomes the limitation of the current methods of classification of cytometry data allowing both semisupervised and supervised annotation based on a marker table that can be defined manually or extracted from an external annotated dataset. We measured the accuracy of GateMeClass for annotating three well-established benchmark mass cytometry datasets and one flow cytometry dataset. The performance of GateMeClass is comparable to reference-based methods and marker table-based techniques, offering greater flexibility and rapid execution times.

AVAILABILITY AND IMPLEMENTATION

GateMeClass is implemented in R language and is publicly available at https://github.com/simo1c/GateMeClass.

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).

COVID-19 vaccination induces distinct T-cell responses in pediatric solid organ transplant recipients and immunocompetent children

Immune responses to COVID-19 vaccination are attenuated in adult solid organ transplant recipients (SOTRs) and additional vaccine doses are recommended for this population. However, whether COVID-19 mRNA vaccine responses are limited in pediatric SOTRs (pSOTRs) compared to immunocompetent children is unknown. Due to SARS-CoV-2 evolution and mutations that evade neutralizing antibodies, T cells may provide important defense in SOTRs who mount poor humoral responses. Therefore, we assessed anti-SARS-CoV-2 IgG titers, surrogate neutralization, and spike (S)-specific T-cell responses to COVID-19 mRNA vaccines in pSOTRs and their healthy siblings (pHCs) before and after the bivalent vaccine dose. Despite immunosuppression, pSOTRs demonstrated humoral responses to both ancestral strain and Omicron subvariants following the primary ancestral strain monovalent mRNA COVID-19 series and multiple booster doses. These responses were not significantly different from those observed in pHCs and significantly higher six months after vaccination than responses in adult SOTRs two weeks post-vaccination. However, pSOTRs mounted limited S-specific CD8+ T-cell responses and qualitatively distinct CD4+ T-cell responses, primarily producing IL-2 and TNF with less IFN-γ production compared to pHCs. Bivalent vaccination enhanced humoral responses in some pSOTRs but did not shift the CD4+ T-cell responses toward increased IFN-γ production. Our findings indicate that S-specific CD4+ T cells in pSOTRs have distinct qualities with unknown protective capacity, yet vaccination produces cross-reactive antibodies not significantly different from responses in pHCs. Given altered T-cell responses, additional vaccine doses in pSOTRs to maintain high titer cross-reactive antibodies may be important in ensuring protection against SARS-CoV-2.

SUMOylation inhibitors activate anti-tumor immunity by reshaping the immune microenvironment in a preclinical model of hepatocellular carcinoma

PURPOSE

High levels of heterogeneity and immunosuppression characterize the HCC immune microenvironment (TME). Unfortunately, the majority of hepatocellular carcinoma (HCC) patients do not benefit from immune checkpoint inhibitors (ICIs) therapy. New small molecule therapies for the treatment of HCC are the goal of our research.

METHODS

SUMOylation inhibitors (TAK-981 and ML-792) were evaluated for the treatment of preclinical mouse HCC models (including subcutaneous and orthotopic HCC models). We profile immune cell subsets from tumor samples after SUMOylation inhibitors treatment using single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), flow cytometry, and multiple immunofluorescences (mIF).

RESULTS

We discover that SUMOylation is higher in HCC patient samples compared to normal liver tissue. TAK-981 and ML-792 decrease SUMOylation at nanomolar levels in HCC cells and also successfully reduced the tumor burden. Analysis combining scRNA-seq and CyTOF demonstrate that treatment with SUMOylation inhibitors reduces the exhausted CD8+T (Tex) cells while enhancing the cytotoxic NK cells, M1 macrophages and cytotoxic T lymphocytes (CTL) in preclinical mouse HCC model. Furthermore, SUMOylation inhibitors have the potential to activate innate immune signals from CD8+T, NK and macrophages while promoting TNFα and IL-17 secretion. Most notably, SUMOylation inhibitors can directly alter the TME by adjusting the abundance of intestinal microbiota, thereby restoring anti-tumor immunity in HCC models.

CONCLUSIONS

This preclinical study suggests that SUMO signaling inhibitors may be beneficial for the treatment of HCC.

Circulation immune cell landscape in canonical pathogenesis of colorectal adenocarcinoma by CyTOF analysis

Current studies on the immune microenvironment of colorectal cancer (CRC) were mostly limited to the tissue level, lacking relevant studies in the peripheral blood, and failed to describe its alterations in the whole process of adenocarcinoma formation, especially of adenoma carcinogenesis. Here, we constructed a large-scale population cohort and used the CyTOF to explore the changes of various immune cell subsets in peripheral blood of CRC. We found monocytes and basophils cells were significantly higher in adenocarcinoma patients. Compared with early-stage CRC, effector CD4+T cells and naive B cells were higher in patients with lymph node metastasis, whereas the basophils were lower. We also performed random forest algorithm and found monocytes play the key role in carcinogenesis. Our study draws a peripheral blood immune cell landscape of the occurrence and development of CRC at the single-cell level and provides a reference for other researchers.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

Deep Flow Cytometry Unveils Distinct Immune Cell Subsets in Inducible T Cell Co-Stimulator Ligand (ICOSL)- and ICOS-Knockout Mice during Experimental Autoimmune Encephalomyelitis

The inducible T cell co-stimulator ligand (ICOSL), expressed by antigen presenting cells, binds to the inducible T cell co-stimulator (ICOS) on activated T cells. Improper function of the ICOS/ICOSL pathway has been implicated in several autoimmune diseases, including multiple sclerosis (MS). Previous studies showed that ICOS-knockout (KO) mice exhibit severe experimental autoimmune encephalomyelitis (EAE), the animal model of MS, but data on ICOSL deficiency are not available. In our study, we explored the impact of both ICOS and ICOSL deficiencies on MOG35-55 -induced EAE and its associated immune cell dynamics by employing ICOSL-KO and ICOS-KO mice with a C57BL/6J background. During EAE resolution, MOG-driven cytokine levels and the immunophenotype of splenocytes were evaluated by ELISA and multiparametric flow cytometry, respectively. We found that both KO mice exhibited an overlapping and more severe EAE compared to C57BL/6J mice, corroborated by a reduction in memory/regulatory T cell subsets and interleukin (IL-)17 levels. It is noteworthy that an unsupervised analysis showed that ICOSL deficiency modifies the immune response in an original way, by affecting T central and effector memory (TCM, TEM), long-lived CD4+ TEM cells, and macrophages, compared to ICOS-KO and C57BL/6J mice, suggesting a role for other binding partners to ICOSL in EAE development, which deserves further study.

A disrupted FOXP3 transcriptional signature underpins systemic regulatory T cell insufficiency in early pregnancy failure

Regulatory T (Treg) cell defects are implicated in disorders of embryo implantation and placental development, but the origins of Treg cell dysfunction are unknown. Here, we comprehensively analyzed the phenotypes and transcriptional profile of peripheral blood Treg cells in individuals with early pregnancy failure (EPF). Compared to fertile subjects, EPF subjects had 32% fewer total Treg cells and 54% fewer CD45RA+CCR7+ naive Treg cells among CD4+ T cells, an altered Treg cell phenotype with reduced transcription factor FOXP3 and suppressive marker CTLA4 expression, and lower Treg:Th1 and Treg:Th17 ratios. RNA sequencing demonstrated an aberrant gene expression profile, with upregulation of pro-inflammatory genes including CSF2, IL4, IL17A, IL21, and IFNG in EPF Treg cells. In silico analysis revealed 25% of the Treg cell dysregulated genes are targets of FOXP3. We conclude that EPF is associated with systemic Treg cell defects arising due to disrupted FOXP3 transcriptional control and loss of lineage fidelity.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data

A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce adaptive reweighting, a heuristic procedure tailored to single-cell data-including a marker-aware version-that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader .

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

CD57-positive CD8 + T cells define the response to anti-programmed cell death protein-1 immunotherapy in patients with advanced non-small cell lung cancer

Immune checkpoint inhibitors have transformed the treatment landscape of non-small cell lung cancer (NSCLC). However, accurately identifying patients who will benefit from immunotherapy remains a challenge. This study aimed to discover potential biomarkers for predicting immunotherapy response in NSCLC patients. Single-cell mass cytometry (CyTOF) was utilized to analyze immune cell subsets in peripheral blood mononuclear cells (PBMCs) obtained from NSCLC patients before and 12 weeks after single-agent immunotherapy. The CyTOF findings were subsequently validated using flow cytometry and multiplex immunohistochemistry/immunofluorescence in PBMCs and tumor tissues, respectively. RNA sequencing (RNA-seq) was performed to elucidate the underlying mechanisms. In the CyTOF cohort (n = 20), a high frequency of CD57+CD8+ T cells in PBMCs was associated with durable clinical benefit from immunotherapy in NSCLC patients (p = 0.034). This association was further confirmed in an independent cohort using flow cytometry (n = 27; p < 0.001), with a determined cutoff value of 12.85%. The cutoff value was subsequently validated in another independent cohort (AUC = 0.733). We also confirmed the CyTOF findings in pre-treatment formalin-fixed and paraffin-embedded tissues (n = 90; p < 0.001). RNA-seq analysis revealed 475 differentially expressed genes (DEGs) between CD57+CD8+ T cells and CD57-CD8+ T cells, with functional analysis identifying DEGs significantly enriched in immune-related signaling pathways. This study highlights CD57+CD8+ T cells as a promising biomarker for predicting immunotherapy success in NSCLC patients.

Emergency myelopoiesis distinguishes multisystem inflammatory syndrome in children from pediatric severe COVID-19

BACKGROUND

Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory condition caused by recent SARS-CoV-2 infection, but the underlying immunological mechanisms driving this distinct syndrome are unknown.

METHODS

We utilized high dimensional flow cytometry, cell-free (cf) DNA, and cytokine and chemokine profiling to identify mechanisms of critical illness distinguishing MIS-C from severe acute COVID-19 (SAC).

RESULTS

Compared to SAC, MIS-C patients demonstrated profound innate immune cell death and features of emergency myelopoiesis (EM), an understudied phenomenon observed in severe inflammation. EM signatures were characterized by fewer mature myeloid cells in the periphery and decreased expression of HLA-DR and CD86 on antigen presenting cells. IL-27, a cytokine known to drive hematopoietic stem cells towards EM, was increased in MIS-C, and correlated with immature cell signatures in MIS-C. Upon recovery, EM signatures decreased, and IL-27 plasma levels returned to normal levels. Despite profound lymphopenia, we report a lack of cfDNA released by adaptive immune cells and increased CCR7 expression on T cells indicative of egress out of peripheral blood.

CONCLUSIONS

Immune cell signatures of EM combined with elevated innate immune cell-derived cfDNA levels distinguish MIS-C from SAC in children and provide mechanistic insight into dysregulated immunity contributing towards MIS-C, offering potential diagnostic and therapeutic targets.

Unveiling Macrophage Heterogeneity and Their Spatial Distribution Using Multiplexed Tissue Imaging

Macrophages display a high degree of phenotypic diversity and plasticity, which is influenced by their location within the tissue microenvironment. Co-Detection by Indexing (CODEX), a multiplexed imaging technique, allows the simultaneous detection of multiple membrane and cellular markers that enable the accurate identification of tissue-resident hematopoietic and non-hematopoietic cells, while conferring spatial information at a single-cell level. Here we describe the use of CODEX to visualize the phenotypic and spatial heterogeneity of murine tissue-resident macrophages in several organs, and a pipeline to characterize their cellular microenvironments and interactions.

Heterogeneity and transcriptional drivers of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

Unbiased high-dimensional flow cytometry identified NK and DC immune cell signature in Luminal A-type and triple negative breast cancer

Breast cancer is the most common malignancy in women worldwide and a highly heterogeneous disease. Four different subtypes are described that differ in the expression of hormone receptors as well as the growth factor receptor HER2. Treatment modalities and survival rate depend on the subtype of breast cancer. However, it is still not clear which patients benefit from immunotherapeutic approaches such as checkpoint blockade. Thus, we aimed to decipher the immune cell signature of the different breast cancer subtypes based on high-dimensional flow cytometry followed by unbiased approaches. Here, we show that the frequency of NK cells is reduced in Luminal A and B as well as triple negative breast cancer and that the phenotype of residual NK cells is changed toward regulatory CD11b-CD16- NK cells. Further, we found higher frequencies of PD-1+ CD4+ and CD8+ T cells in triple negative breast cancer. Moreover, while Luminal A-type breast cancer was enriched for CD14+ cDC2 (named type 3 DC (DC3)), CD14- cDC2 (named DC2) were more frequent in triple negative breast cancer. In contrast, HER2-enriched breast cancer did not show major alterations in the composition of the immune cell compartment in the tumor microenvironment. These findings suggest that patients with Luminal A- and B-type as well as triple negative breast cancer might benefit from immunotherapeutic approaches targeting NK cells.

Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry

Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.

Characteristics of circulating immune cells in HBV-related acute-on-chronic liver failure following artificial liver treatment

BACKGROUND AND AIM

Liver failure, which is predominantly caused by hepatitis B (HBV) can be improved by an artificial liver support system (ALSS). This study investigated the phenotypic heterogeneity of immunocytes in patients with HBV-related acute-on-chronic liver failure (HBV-ACLF) before and after ALSS therapy.

METHODS

A total of 22 patients with HBV-ACLF who received ALSS therapy were included in the study. Patients with Grade I according to the ACLF Research Consortium score were considered to have improved. Demographic and laboratory data were collected and analyzed during hospitalization. Immunological features of peripheral blood in the patients before and after ALSS were detected by mass cytometry analyses.

RESULTS

In total, 12 patients improved and 10 patients did not. According to the immunological features data after ALSS, the proportion of circulating monocytes was significantly higher in non-improved patients, but there were fewer γδT cells compared with those in improved patients. Characterization of 37 cell clusters revealed that the frequency of effector CD8+ T (P = 0.003), CD4+ TCM (P = 0.033), CD4+ TEM (P = 0.039), and inhibitory natural killer (NK) cells (P = 0.029) decreased in HBV-ACLF patients after ALSS therapy. Sub group analyses after treatment showed that the improved patients had higher proportions of CD4+ TCM (P = 0.010), CD4+ TEM (P = 0.021), and γδT cells (P = 0.003) and a lower proportion of monocytes (P = 0.012) compared with the non-improved patients.

CONCLUSIONS

Changes in effector CD8+ T cells, effector and memory CD4+ T cells, and inhibitory NK cells are associated with ALSS treatment of HBV-ACLF. Moreover, monocytes and γδT cells exhibited the main differences when patients obtained different prognoses. The phenotypic heterogeneity of lymphocytes and monocytes may contribute to the prognosis of ALSS and future immunotherapy strategies.

The effect of dosage on the protective efficacy of whole-sporozoite formulations for immunization against malaria

Immunization with Plasmodium sporozoites, either attenuated or administered under the cover of an antimalarial drug, can induce strong protection against malaria in pre-clinical murine models, as well as in human trials. Previous studies have suggested that whole-sporozoite (WSpz) formulations based on parasites with longer liver stage development induce higher protection, but a comparative analysis of four different WSpz formulations has not been reported. We employed a rodent model of malaria to analyze the effect of immunization dosage on the protective efficacy of WSpz formulations consisting of (i) early liver arresting genetically attenuated parasites (EA-GAP) or (ii) radiation-attenuated sporozoites (RAS), (iii) late arresting GAP (LA-GAP), and (iv) sporozoites administered under chemoprophylaxis, that are eliminated upon release into the bloodstream (CPS). Our results show that, unlike all other WSpz formulations, EA-GAP fails to confer complete protection against an infectious challenge at any immunization dosage employed, suggesting that a minimum threshold of liver development is required to elicit fully effective immune responses. Moreover, while immunization with RAS, LA-GAP and CPS WSpz yields comparable, dosage-dependent protection, protection by EA-GAP WSpz peaks at an intermediate dosage and markedly decreases thereafter. In-depth immunological analyses suggest that effector CD8+ T cells elicited by EA-GAP WSpz immunization have limited developmental plasticity, with a potential negative impact on the functional versatility of memory cells and, thus, on protective immunity. Our findings point towards dismissing EA-GAP from prioritization for WSpz malaria vaccination and enhance our understanding of the complexity of the protection elicited by these WSpz vaccine candidates, guiding their future optimization.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data

To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented information about the protein expression of a single cell. Conversely, the analysis of high-dimensional and multiparametric mass cytometric data sets presents a new computational challenge. For instance, conventional "manual gating" analysis was inefficient and unreliable for multiparametric phenotyping of the heterogeneous immune cellular system; consequently, automated methods have been developed to address the high dimensionality of mass cytometry data and enhance the reproducibility of the analysis. Here, we present CyGate, a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. CyGate is available at https://github.com/seungjinna/cygate.

High-dimensional mass cytometry reveals systemic and local immune signatures in necrotizing enterocolitis

Objective

Patients with necrotizing enterocolitis display severe gastrointestinal complications of prematurity, but the mechanism driving this clinical profile remains unknown. We used mass cytometry time-of-flight to characterize and compare immune cell populations in the blood and intestine tissue from patients with and without (controls) necrotizing enterocolitis at single-cell resolution.

Methods

We completed a deep mapping of the immune system of the peripheral blood mononuclear cells and intestinal mucosa tissue using mass cytometry to evaluate immune cell types, which revealed global immune dysregulation characteristics underlying necrotizing enterocolitis.

Results

Compared with controls, natural killer cells display signs of heightened activation and increased cytotoxic potential in the peripheral blood and mucosa of patients with necrotizing enterocolitis. Furthermore, CD4+ T effector memory cells, non-classical monocytes, active dendritic cells, and neutrophils were specifically enriched in the mucosa, suggesting trafficking from the periphery to areas of inflammation. Moreover, we mapped the systemic and local distinct immune signatures suggesting patterns of cell localization in necrotizing enterocolitis.

Conclusion

We used mass cytometry time-of-flight technology to identify immune cell populations specific to the peripheral blood and intestinal mucosa tissue from patients with necrotizing enterocolitis and controls. This information might be used to develop precise diagnosis and therapies that target specific cell populations in patients with necrotizing enterocolitis.

Cytomulate: accurate and efficient simulation of CyTOF data

Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

KMD clustering: robust general-purpose clustering of biological data

The noisy and high-dimensional nature of biological data has spawned advanced clustering algorithms that are tailored for specific biological datatypes. However, the performance of such methods varies greatly between datasets and they require post hoc tuning of cryptic hyperparameters. We present k minimal distance (KMD) clustering, a general-purpose method based on a generalization of single and average linkage hierarchical clustering. We introduce a generalized silhouette-like function to eliminate the cryptic hyperparameter k, and use sampling to enable application to million-object datasets. Rigorous comparisons to general and specialized clustering methods on simulated, mass cytometry and scRNA-seq datasets show consistent high performance of KMD clustering across all datasets.

Single-cell and spatial analyses reveal a tradeoff between murine mammary proliferation and lineage programs associated with endocrine cues

Although distinct epithelial cell types have been distinguished in glandular tissues such as the mammary gland, the extent of heterogeneity within each cell type and the degree of endocrine control of this diversity across development are incompletely understood. By combining mass cytometry and cyclic immunofluorescence, we define a rich array of murine mammary epithelial cell subtypes associated with puberty, the estrous cycle, and sex. These subtypes are differentially proliferative and spatially segregate distinctly in adult versus pubescent glands. Further, we identify systematic suppression of lineage programs at the protein and RNA levels as a common feature of mammary epithelial expansion during puberty, the estrous cycle, and gestation and uncover a pervasive enrichment of ribosomal protein genes in luminal cells elicited specifically during progesterone-dominant expansionary periods. Collectively, these data expand our knowledge of murine mammary epithelial heterogeneity and connect endocrine-driven epithelial expansion with lineage suppression.

The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data

Motivation

The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research.

Results

In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis.

Availability and implementation

The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.

A cell-level discriminative neural network model for diagnosis of blood cancers

MOTIVATION

Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes.

RESULTS

We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations.

AVAILABILITY AND IMPLEMENTATION

The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).

scDEED: a statistical method for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

Design, optimisation and standardisation of a high-dimensional spectral flow cytometry workflow assessing T-cell immunophenotype in patients with melanoma

Objectives

Despite the success of immune checkpoint blockade, most metastatic melanoma patients fail to respond to therapy or experience severe toxicity. Assessment of biomarkers and immunophenotypes before or early into treatment will help to understand favourable responses and improve therapeutic outcomes.

Methods

We present a high-dimensional approach for blood T-cell profiling using three multi-parameter cytometry panels: (1) a TruCount panel for absolute cell counts, (2) a 27-colour spectral panel assessing T-cell markers and (3) a 20-colour spectral panel evaluating intracellular cytokine expression. Pre-treatment blood mononuclear cells from patients and healthy controls were cryopreserved before staining across 11 batches. Batch effects were tracked using a single-donor control and the suitability of normalisation was assessed. The data were analysed using manual gating and high-dimensional strategies.

Results

Batch-to-batch variation was minimal, as demonstrated by the dimensionality reduction of batch-control samples, and normalisation did not improve manual or high-dimensional analysis. Application of the workflow demonstrated the capacity of the panels and showed that patients had fewer lymphocytes than controls (P = 0.0027), due to lower naive CD4+ (P = 0.015) and CD8+ (P = 0.011) T cells and follicular helper T cells (P = 0.00076). Patients showed trends for higher proportions of Ki67 and IL-2-expressing cells within CD4+ and CD8+ memory subsets, and increased CD57 and EOMES expression within TCRγδ+ T cells.

Conclusion

Our optimised high-parameter spectral cytometry approach provided in-depth profiling of blood T cells and found differences in patient immunophenotype at baseline. The robustness of our workflow, as demonstrated by minimal batch effects, makes this approach highly suitable for the longitudinal evaluation of immunotherapy effects.

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .

Infectious neutrophil deployment is regulated by resolvin D4

Neutrophils reside in the bone marrow (BM), ready for deployment to sites of injury/infection, initiating inflammation and its resolution. Here, we report that distal infections signal to the BM via resolvins to regulate granulopoiesis and BM neutrophil deployment. Emergency granulopoiesis during peritonitis evoked changes in BM resolvin D1 (RvD1) and BM RvD4. We found that leukotriene B4 stimulates neutrophil deployment. RvD1 and RvD4 each limited neutrophilic infiltration to infections, and differently regulated BM myeloid populations: RvD1 increased reparative monocytes, and RvD4 regulated granulocytes. RvD4 disengaged emergency granulopoiesis, prevented excess BM neutrophil deployment, and acted on granulocyte progenitors. RvD4 also stimulated exudate neutrophil, monocyte, and macrophage phagocytosis, and enhanced bacterial clearance. This mediator accelerated both neutrophil apoptosis and clearance by macrophages, thus expediting the resolution phase of inflammation. RvD4 stimulated phosphorylation of ERK1/2 and STAT3 in human BM-aspirate-derived granulocytes. RvD4 in the 1 to 100 nM range stimulated whole-blood neutrophil phagocytosis of Escherichia coli. RvD4 increased BM macrophage efferocytosis of neutrophils. Together, these results demonstrate the novel functions of resolvins in granulopoiesis and neutrophil deployment, contributing to the resolution of infectious inflammation.

Dynamics of circulating lymphocytes responding to human experimental enterotoxigenic Escherichia coli infection

Enterotoxigenic Escherichia coli (ETEC) is an important cause of children's and travelers' diarrhea, with no licensed vaccine. This study aimed to explore the role of cellular immunity in protection against human ETEC infection. Nine volunteers were experimentally infected with ETEC, of which six developed diarrhea. Lymphocytes were collected from peripheral blood buffy coats, before and 3, 5, 6, 7, 10, and 28 days after dose ingestion, and 34 phenotypic and functional markers were examined by mass cytometry. Thirty-three cell populations, derived by manually merging 139 cell clusters from the X-shift unsupervised clustering algorithm, were analyzed. Initially, the diarrhea group responded with increased CD56dim CD16+ natural killer cells, dendritic cells tended to rise, and mucosal-associated invariant T cells decreased. On day 5-7, an increase in plasmablasts was paralleled by a consistent rise in CD4+ Th17-like effector memory and regulatory cell subsets. CD4+ Th17-like central memory cells peaked on day 10. All Th17-like cell populations showed increased expression of activation, gut-homing, and proliferation markers. Interestingly, in the nondiarrhea group, these same CD4+ Th17-like cell populations expanded earlier, normalizing around day 7. Earlier development of these CD4+ Th17-like cell populations in the nondiarrhea group may suggest a recall response and a potential role in controlling ETEC infections.

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering

While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

Rapid recruitment and IFN-I-mediated activation of monocytes dictate focal radiotherapy efficacy

The recruitment of monocytes and their differentiation into immunosuppressive cells is associated with the low efficacy of preclinical nonconformal radiotherapy (RT) for tumors. However, nonconformal RT (non-CRT) does not mimic clinical practice, and little is known about the role of monocytes after RT modes used in patients, such as conformal RT (CRT). Here, we investigated the acute immune response induced by after CRT. Contrary to non-CRT approaches, we found that CRT induces a rapid and robust recruitment of monocytes to the tumor that minimally differentiate into tumor-associated macrophages or dendritic cells but instead up-regulate major histocompatibility complex II and costimulatory molecules. We found that these large numbers of infiltrating monocytes are responsible for activating effector polyfunctional CD8+ tumor-infiltrating lymphocytes that reduce tumor burden. Mechanistically, we show that monocyte-derived type I interferon is pivotal in promoting monocyte accumulation and immunostimulatory function in a positive feedback loop. We also demonstrate that monocyte accumulation in the tumor microenvironment is hindered when RT inadvertently affects healthy tissues, as occurs in non-CRT. Our results unravel the immunostimulatory function of monocytes during clinically relevant modes of RT and demonstrate that limiting the exposure of healthy tissues to radiation has a positive therapeutic effect on the overall antitumor immune response.

Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments

The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then generate melanoma histocytometry whole slide images and verify that CISA can detect similar interactions across data modalities. Interestingly, CISA histoctyometry analysis also reveals that T-cell:macrophage synapse formation is associated with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types.

Functional CVIDs phenotype clusters identified by the integration of immune parameters after BNT162b2 boosters

Introduction

Assessing the response to vaccinations is one of the diagnostic criteria for Common Variable Immune Deficiencies (CVIDs). Vaccination against SARS-CoV-2 offered the unique opportunity to analyze the immune response to a novel antigen. We identify four CVIDs phenotype clusters by the integration of immune parameters after BTN162b2 boosters.

Methods

We performed a longitudinal study on 47 CVIDs patients who received the 3rd and 4th vaccine dose of the BNT162b2 vaccine measuring the generation of immunological memory. We analyzed specific and neutralizing antibodies, spike-specific memory B cells, and functional T cells.

Results

We found that, depending on the readout of vaccine efficacy, the frequency of responders changes. Although 63.8% of the patients have specific antibodies in the serum, only 30% have high-affinity specific memory B cells and generate recall responses.

Discussion

Thanks to the integration of our data, we identified four functional groups of CVIDs patients with different B cell phenotypes, T cell functions, and clinical diseases. The presence of antibodies alone is not sufficient to demonstrate the establishment of immune memory and the measurement of the in-vivo response to vaccination distinguishes patients with different immunological defects and clinical diseases.

Breaking Spatiotemporal Barriers of Immunogenic Chemotherapy via an Endoplasmic Reticulum Membrane-Assisted Liposomal Drug Delivery

Immunogenic chemotherapy is a promising approach in cancer treatment, but the number of drugs capable of inducing immunogenic cell death is limited, and chronic immunogenic exposure can delay antitumor immune response and be counteracted by immunosuppressive factors. In this study, we used single-cell and multilevel analyses to highlight the critical importance of the first exposure to calreticulin (CRT) in eliciting immunogenicity. We then developed the ERASION (endoplasmic reticulum (ER) membrane to assist (AS) the presentation of intrinsic onco-immunogenicity (ION)) strategy, leveraging the high expression of functional proteins, including CRT, on the ER membrane. ER membrane-coated liposome (ER@PLip) was able to target the tumor and immune effectors and promoted dendritic cell maturation and T cell infiltration. This enabled eliciting an immunogenic effect from a nonimmunogenic chemotherapeutic drug. By utilizing the ER membrane-associated STING protein, ERASION enabled activating the STING pathway and the generation of adaptive antitumor immunity. This study presents a potential universal platform for integrating traditional chemotherapy and therapeutic modalities.

CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis

With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including PhenoGraph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.