QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.

Plasmodium infection induces phenotypic, clonal, and spatial diversity among differentiating CD4+ T cells

Naive CD4+ T cells must differentiate in order to orchestrate immunity to Plasmodium, yet understanding of their emerging phenotypes, clonality, spatial distributions, and cellular interactions remains incomplete. Here, we observe that splenic polyclonal CD4+ T cells differentiate toward T helper 1 (Th1) and T follicular helper (Tfh)-like states and exhibit rarer phenotypes not elicited among T cell receptor (TCR) transgenic counterparts. TCR clones present at higher frequencies exhibit Th1 skewing, suggesting that variation in major histocompatibility complex class II (MHC-II) interaction influences proliferation and Th1 differentiation. To characterize CD4+ T cell interactions, we map splenic microarchitecture, cellular locations, and molecular interactions using spatial transcriptomics at near single-cell resolution. Tfh-like cells co-locate with stromal cells in B cell follicles, while Th1 cells in red pulp co-locate with activated monocytes expressing multiple chemokines and MHC-II. Spatial mapping of individual transcriptomes suggests that proximity to chemokine-expressing monocytes correlates with stronger effector phenotypes in Th1 cells. Finally, CRISPR-Cas9 gene disruption reveals a role for CCR5 in promoting clonal expansion and Th1 differentiation. A database of cellular locations and interactions is presented: https://haquelab.mdhs.unimelb.edu.au/spatial_gui/.

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.

Single-cell atlas reveals characteristic changes in intrahepatic HBV-specific leukocytes

IMPORTANCE

Hepatitis B virus (HBV)-specific CD8+ T cells play a central role in the clearance of virus and HBV-related liver injury. Acute infection with HBV induces a vigorous, multifunctional CD8+ T cell response, whereas chronic one exhibits a weaker response. Our study elucidated HBV-specific T cell responses in terms of viral abundance rather than the timing of infection. We showed that in the premalignant stage, the degree of impaired T cell function was not synchronized with the viral surface antigen, which was attributed the liver's tolerance to the virus. However, after the development of hepatocellular carcinoma, T cell exhaustion was inevitable, and it was marked by the exhaustion of the signature transcription factor TOX.

TGF-β-p-STAT1-LAIR2 axis has a "self-rescue" role for exhausted CD8+ T cells in hepatocellular carcinoma

BACKGROUND

TGF-β is related to the function of T cells in the tumor microenvironment. However, the characteristics of TGF-β affecting the function of CD8+ T cells in hepatocellular carcinoma (HCC) have not been clearly resolved.

METHODS

In this study, flow cytometry, mass cytometry, immunohistochemistry, RNA-seq, single-cell RNA-seq, assay for transposase-accessible chromatin with high throughput sequencing, chromatin immunoprecipitation, and dual-luciferase reporter gene assay were used to study the regulatory effect and molecular mechanism of TGF-β on HCC infiltrating CD8+ T cells.

RESULTS

Here, we demonstrated that the overall effect of TGF-β on CD8+ T cells in HCC was to activate p-p38 to induce exhaustion, but it also initiated cell-intrinsic resistance mechanisms: 1) TGF-β upregulated the levels of p-STAT1 (S727) and promoted LAIR2 secretion; 2) the TGF-β-p-STAT1-LAIR2 axis relieved CD8+ T cells from exhaustion, which we called "self-rescue"; 3) this "self-rescue" behavior showed time and dose limitations on TGF-β stimulation, which was easily masked by stronger inhibitory signals; 4) the function of CD8+ T cells was improved by using TAK-981 to amplify "self-rescue" signal.

CONCLUSION

Our study describes a "self-rescue" mechanism of CD8+ T cells in HCC against exhaustion and the good effects from amplifying this signal.

Targeted inhibition of RBPJ transcription complex alleviates the exhaustion of CD8+ T cells in hepatocellular carcinoma

Impaired function of CD8+ T cells in hepatocellular carcinoma (HCC) is an important reason for acquired resistance. Compared with single-target inhibitors, small-molecule compounds that could both inhibit tumor cells and alleviate T cell exhaustion are more promising to reduce resistance. In this study, we screened immunosuppressive targets in HCC by combining cancer-immunity cycle score with weighted gene co-expression network and system analysis. Through in vitro and in vivo validation experiments, we found that one of the screened molecules, recombination signal binding protein for immunoglobulin kappa J region (RBPJ), was negatively correlated with CD8+ T cell mediated killing function. More importantly, its transcription complex inhibitor RIN1 not only inhibited the malignant biological behaviors of HCC cells by inhibiting mTOR pathway, but also reduced the expression of PD-L1 and L-kynurenine synthesis in HCC cells, thus alleviating T cell exhaustion. Meanwhile, the combination of RIN1 and anti-PD-1/PD-L1 antibodies could further activate CD8+ T cells. In short, RBPJ is an important factor regulating the function of T cells. Target inhibition of RBPJ transcription complex by small molecule compound may be a new strategy for immunotherapy of HCC.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Flow cytometry data analysis: Recent tools and algorithms

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.

Data-Driven Flow Cytometry Analysis

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.

CymeR: cytometry analysis using KNIME, docker and R

Summary

Here we present open-source software for the analysis of high-dimensional cytometry data using state of the art algorithms. Importantly, use of the software requires no programming ability, and output files can either be interrogated directly in CymeR or they can be used downstream with any other cytometric data analysis platform. Also, because we use Docker to integrate the multitude of components that form the basis of CymeR, we have additionally developed a proof-of-concept of how future open-source bioinformatic programs with graphical user interfaces could be developed.

Availability and Implementation

CymeR is open-source software that ties several components into a single program that is perhaps best thought of as a self-contained data analysis operating system. Please see https://github.com/bmuchmore/CymeR/wiki for detailed installation instructions.

Contact

brian.muchmore@genyo.es or marta.alarcon@genyo.es.

Recent advances in quantitative high throughput and high content data analysis

INTRODUCTION

High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens.

AREAS COVERED

This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data.

EXPERT OPINION

As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.

Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery

Multi-color flow cytometry has become a valuable and highly informative tool for diagnosis and therapeutic monitoring of patients with immune deficiencies or inflammatory disorders. However, the method complexity and error-prone conventional manual data analysis often result in a high variability between different analysts and research laboratories. Here, we provide strategies and guidelines aiming at a more standardized multi-color flow cytometric staining and unsupervised data analysis for whole blood patient samples.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

High-throughput flow cytometry data normalization for clinical trials

Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially if a template-gating approach is used without further correction to the gates. In this article, a computational framework is presented for normalizing the fluorescence intensity of multiple markers in specific cell populations across samples that is suitable for high-throughput processing of large clinical trial datasets. Previous approaches to normalization have been global and applied to all cells or data with debris removed. They provided no mechanism to handle specific cell subsets. This approach integrates tightly with the gating process so that normalization is performed during gating and is local to the specific cell subsets exhibiting variability. This improves peak alignment and the performance of the algorithm. The performance of this algorithm is demonstrated on two clinical trial datasets from the HIV Vaccine Trials Network (HVTN) and the Immune Tolerance Network (ITN). In the ITN data set we show that local normalization combined with template gating can account for sample-to-sample variability as effectively as manual gating. In the HVTN dataset, it is shown that local normalization mitigates false-positive vaccine response calls in an intracellular cytokine staining assay. In both datasets, local normalization performs better than global normalization. The normalization framework allows the use of template gates even in the presence of sample-to-sample staining variability, mitigates the subjectivity and bias of manual gating, and decreases the time necessary to analyze large datasets.

Comparability and reproducibility of biomedical data

With the development of novel assay technologies, biomedical experiments and analyses have gone through substantial evolution. Today, a typical experiment can simultaneously measure hundreds to thousands of individual features (e.g. genes) in dozens of biological conditions, resulting in gigabytes of data that need to be processed and analyzed. Because of the multiple steps involved in the data generation and analysis and the lack of details provided, it can be difficult for independent researchers to try to reproduce a published study. With the recent outrage following the halt of a cancer clinical trial due to the lack of reproducibility of the published study, researchers are now facing heavy pressure to ensure that their results are reproducible. Despite the global demand, too many published studies remain non-reproducible mainly due to the lack of availability of experimental protocol, data and/or computer code. Scientific discovery is an iterative process, where a published study generates new knowledge and data, resulting in new follow-up studies or clinical trials based on these results. As such, it is important for the results of a study to be quickly confirmed or discarded to avoid wasting time and money on novel projects. The availability of high-quality, reproducible data will also lead to more powerful analyses (or meta-analyses) where multiple data sets are combined to generate new knowledge. In this article, we review some of the recent developments regarding biomedical reproducibility and comparability and discuss some of the areas where the overall field could be improved.

Computational analysis of high-throughput flow cytometry data

INTRODUCTION

Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology.

AREAS COVERED

Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension.

EXPERT OPINION

There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.