Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

Integrative multiomics enhancer activity profiling identifies therapeutic vulnerabilities in cholangiocarcinoma of different etiologies

OBJECTIVES

Cholangiocarcinoma (CCA) is a heterogeneous malignancy with high mortality and dismal prognosis, and an urgent clinical need for new therapies. Knowledge of the CCA epigenome is largely limited to aberrant DNA methylation. Dysregulation of enhancer activities has been identified to affect carcinogenesis and leveraged for new therapies but is uninvestigated in CCA. Our aim is to identify potential therapeutic targets in different subtypes of CCA through enhancer profiling.

DESIGN

Integrative multiomics enhancer activity profiling of diverse CCA was performed. A panel of diverse CCA cell lines, patient-derived and cell line-derived xenografts were used to study identified enriched pathways and vulnerabilities. NanoString, multiplex immunohistochemistry staining and single-cell spatial transcriptomics were used to explore the immunogenicity of diverse CCA.

RESULTS

We identified three distinct groups, associated with different etiologies and unique pathways. Drug inhibitors of identified pathways reduced tumour growth in in vitro and in vivo models. The first group (ESTRO), with mostly fluke-positive CCAs, displayed activation in estrogen signalling and were sensitive to MTOR inhibitors. Another group (OXPHO), with mostly BAP1 and IDH-mutant CCAs, displayed activated oxidative phosphorylation pathways, and were sensitive to oxidative phosphorylation inhibitors. Immune-related pathways were activated in the final group (IMMUN), made up of an immunogenic CCA subtype and CCA with aristolochic acid (AA) mutational signatures. Intratumour differences in AA mutation load were correlated to intratumour variation of different immune cell populations.

CONCLUSION

Our study elucidates the mechanisms underlying enhancer dysregulation and deepens understanding of different tumourigenesis processes in distinct CCA subtypes, with potential significant therapeutics and clinical benefits.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny

BACKGROUND

The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data.

RESULTS

We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations.

CONCLUSIONS

CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Enhanced annotation of CD45RA to distinguish T cell subsets in single-cell RNA-seq via machine learning

Motivation

T cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of surface protein markers. With the advent of single-cell RNA sequencing (scRNA-seq), researchers can now investigate the gene expression profiles of these surface proteins at the single-cell level. The insights gleaned from these profiles offer valuable clues and a deeper understanding of cell identity. However, CD45RA, the isoform of CD45 which distinguishes between naive/central memory T cells and effector memory/effector memory cells re-expressing CD45RA T cells, cannot be well profiled by scRNA-seq due to the difficulty in mapping short reads to genes.

Results

In order to facilitate cell-type annotation in T cell scRNA-seq analysis, we employed machine learning and trained a CD 45 RA + / - classifier on single-cell mRNA count data annotated with known CD45RA antibody levels provided by cellular indexing of transcriptomes and epitopes sequencing data. Among all the algorithms we tested, the trained support vector machine with a radial basis function kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer perceptron classifier, the second most predictive method overall, also achieved a decent accuracy of 99.74%. Our simple yet robust machine learning approach provides a valid inference on the CD45RA level, assisting the cell identity annotation and further exploring the heterogeneity within human T cells. Based on the overall performance, we chose the support vector machine with a radial basis function kernel as the model implemented in our Python package scCD45RA.

Availability and implementation

The resultant package scCD45RA can be found at https://github.com/BrubakerLab/ScCD45RA and can be installed from the Python Package Index (PyPI) using the command "pip install sccd45ra."

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics

BACKGROUND

The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called 'spatial transcriptomics'. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce.

RESULTS

Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies.

CONCLUSIONS

Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.

Single-Cell RNAseq Profiling of Human γδ T Lymphocytes in Virus-Related Cancers and COVID-19 Disease

The detailed characterization of human γδ T lymphocyte differentiation at the single-cell transcriptomic (scRNAseq) level in tumors and patients with coronavirus disease 2019 (COVID-19) requires both a reference differentiation trajectory of γδ T cells and a robust mapping method for additional γδ T lymphocytes. Here, we incepted such a method to characterize thousands of γδ T lymphocytes from (n = 95) patients with cancer or adult and pediatric COVID-19 disease. We found that cancer patients with human papillomavirus-positive head and neck squamous cell carcinoma and Epstein-Barr virus-positive Hodgkin's lymphoma have γδ tumor-infiltrating T lymphocytes that are more prone to recirculate from the tumor and avoid exhaustion. In COVID-19, both TCRVγ9 and TCRVγnon9 subsets of γδ T lymphocytes relocalize from peripheral blood mononuclear cells (PBMC) to the infected lung tissue, where their advanced differentiation, tissue residency, and exhaustion reflect T cell activation. Although severe COVID-19 disease increases both recruitment and exhaustion of γδ T lymphocytes in infected lung lesions but not blood, the anti-IL6R therapy with Tocilizumab promotes γδ T lymphocyte differentiation in patients with COVID-19. PBMC from pediatric patients with acute COVID-19 disease display similar γδ T cell lymphopenia to that seen in adult patients. However, blood γδ T cells from children with the COVID-19-related multisystem inflammatory syndrome are not lymphodepleted, but they are differentiated as in healthy PBMC. These findings suggest that some virus-induced memory γδ T lymphocytes durably persist in the blood of adults and could subsequently infiltrate and recirculate in tumors.

Phased differentiation of γδ T and T CD8 tumor-infiltrating lymphocytes revealed by single-cell transcriptomics of human cancers

γδ T lymphocytes diverge from conventional T CD8 lymphocytes for ontogeny, homing, and antigen specificity, but whether their differentiation in tumors also deviates was unknown. Using innovative analyses of our original and ~150 published single-cell RNA sequencing datasets validated by phenotyping of human tumors and murine models, here we present the first high-resolution view of human γδ T cell differentiation in cancer. While γδ T lymphocytes prominently encompass TCRVγ9 cells more differentiated than T CD8 in healthy donor’s blood, a different scenario is unveiled in tumors. Solid tumors and lymphomas are infiltrated by a majority of TCRVγnon9 γδ T cells which are quantitatively correlated and remarkably aligned with T CD8 for differentiation, exhaustion, gene expression profile, and response to immune checkpoint therapy. This cancer-wide association is critical for developing cancer immunotherapies.

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

Lymphoma Heterogeneity Unraveled by Single-Cell Transcriptomics

High-definition transcriptomic studies through single-cell RNA sequencing (scRNA-Seq) have revealed the heterogeneity and functionality of the various microenvironments across numerous solid tumors. Those pioneer studies have highlighted different cellular signatures correlated with clinical response to immune checkpoint inhibitors. scRNA-Seq offers also a unique opportunity to unravel the intimate heterogeneity of the ecosystems across different lymphoma entities. In this review, we will first cover the basics and future developments of the technology, and we will discuss its input in the field of translational lymphoma research, from determination of cell-of-origin and functional diversity, to monitoring of anti-cancer targeted drugs response and toxicities, and how new improvements in both data collection and interpretation will further foster precision medicine in the upcoming years.

PD-1 blockade restores helper activity of tumor-infiltrating, exhausted PD-1hiCD39+ CD4 T cells

Tumor antigen-specific CD4 T cells accumulate at tumor sites, evoking their involvement in antitumor effector functions in situ. Contrary to CD8 cytotoxic T lymphocyte exhaustion, that of CD4 T cells remains poorly appreciated. Here, using phenotypic, transcriptomic, and functional approaches, we characterized CD4 T cell exhaustion in patients with head and neck, cervical, and ovarian cancer. We identified a CD4 tumor-infiltrating lymphocyte (TIL) population, defined by high PD-1 and CD39 expression, which contained high proportions of cytokine-producing cells, although the quantity of cytokines produced by these cells was low, evoking an exhausted state. Terminal exhaustion of CD4 TILs was instated regardless of TIM-3 expression, suggesting divergence with CD8 T cell exhaustion. scRNA-Seq and further phenotypic analyses uncovered similarities with the CD8 T cell exhaustion program. In particular, PD-1hiCD39+ CD4 TILs expressed the exhaustion transcription factor TOX and the chemokine CXCL13 and were tumor antigen specific. In vitro, PD-1 blockade enhanced CD4 TIL activation, as evidenced by increased CD154 expression and cytokine secretion, leading to improved dendritic cell maturation and consequently higher tumor-specific CD8 T cell proliferation. Our data identify exhausted CD4 TILs as players in responsiveness to immune checkpoint blockade.

Longitudinal CITE-Seq profiling of chronic lymphocytic leukemia during ibrutinib treatment: evolution of leukemic and immune cells at relapse

BACKGROUND

Ibrutinib, an irreversible Bruton Tyrosine Kinase (BTK) inhibitor, has revolutionized Chronic Lymphocytic Leukemia (CLL) treatment, but resistances to ibrutinib have emerged, whether related or not to BTK mutations. Patterns of CLL evolution under ibrutinib therapy are well characterized for the leukemic cells but not for their microenvironment.

METHODS

Here, we addressed this question at the single cell level of both transcriptome and immune-phenotype. The PBMCs from a CLL patient were monitored during ibrutinib treatment using Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-Seq) technology.

RESULTS

This unveiled that the short clinical relapse of this patient driven by BTK mutation is associated with intraclonal heterogeneity in B leukemic cells and up-regulation of common signaling pathways induced by ibrutinib in both B leukemic cells and immune cells. This approach also pinpointed a subset of leukemic cells present before treatment and highly enriched during progression under ibrutinib. These latter exhibit an original gene signature including up-regulated BCR, MYC-activated, and other targetable pathways. Meanwhile, although ibrutinib differentially affected the exhaustion of T lymphocytes, this treatment enhanced the T cell cytotoxicity even during disease progression.

CONCLUSIONS

These results could open new alternative of therapeutic strategies for ibrutinib-refractory CLL patients, based on immunotherapy or targeting B leukemic cells themselves.