CD3-positive B cells: a storage-dependent phenomenon

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

Flowcytometric data of intermediate-large cell gastrointestinal lymphoma presenting a gross mass in 32 cats - "let them glow in the flow"

Gastrointestinal lymphoma is the most common form of lymphoma in domestic cats. Aggressive phenotypes are much less common but do bear and unfavorable prognosis. Immunophenotyping by flow cytometry (FCM) is not systematically performed in these patients, because of difficulties in the acquisition of suitable sample material from the gastrointestinal tract. A multimodal diagnostic approach is recommended to improve identification of subtypes targeting patient tailored therapeutic strategies. The aim of this prospective study was to present results of multicolor FCM immunophenotyping in surgically removed gastrointestinal mass and relate them with histopathology using the World Health Organization (WHO) classification and clonality PCR testing. Thirty-two patients were included. Eight cats (25%) had gastric, 23 (72%) had intestinal lymphoma and 1 (3%) had gastric/jejunal lymphoma. Intestinal lymphoma sites were represented by 18 small intestinal, 4 ileocaecal, 1 large intestinal. All gastric lymphomas were diffuse large B-cell lymphoma (DLBCL). Small intestinal lymphomas were 10 enteropathy associated T-cell lymphoma type I (EATL I), 2 enteropathy associated T-cell lymphoma type II (EATL II), 2 peripheral T-cell lymphoma (PTCL), 3 DLBCL and one DLBCL+EATL II. The most common small intestinal FCM T-cell phenotype was CD3+CD21- CD4-CD8-CD18+ CD5-CD79- in 7/10 EATL I and one EATL II. The most frequent FCM B-cell phenotype was CD3-CD21+ CD4-CD8-CD18+ CD5-CD79+ in 13/17 DLBCL and the DLBCL+EATL II. Clonality PCR results were positive in 87.5% (28/32) of all cases. No cross-lineage rearrangement was observed. IHC and FCM results agreed in 87.5% (28/32) of all cases. When all 3 methods were combined, consistent results were seen in 75% (24/32). This is the first demonstration of a multicolor FCM approach set in context to the gold standard histopathology and clonality testing results.

High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology

This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.

An angel or a devil? Current view on the role of CD8+ T cells in the pathogenesis of myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) and the experimental autoimmune MG (EAMG) animal model are characterized by T-cell-induced and B-cell-dominated autoimmune diseases that affect the neuromuscular junction. Several subtypes of CD4+ T cells, including T helper (Th) 17 cells, follicular Th cells, and regulatory T cells (Tregs), contribute to the pathogenesis of MG. However, increasing evidence suggests that CD8+ T cells also play a critical role in the pathogenesis and treatment of MG.

MAIN BODY

Herein, we review the literature on CD8+ T cells in MG, focusing on their potential effector and regulatory roles, as well as on relevant evidence (peripheral, in situ, cerebrospinal fluid, and under different treatments), T-cell receptor usage, cytokine and chemokine expression, cell marker expression, and Treg, Tc17, CD3+CD8+CD20+ T, and CXCR5+ CD8+ T cells.

CONCLUSIONS

Further studies on CD8+ T cells in MG are necessary to determine, among others, the real pattern of the Vβ gene usage of autoantigen-specific CD8+ cells in patients with MG, real images of the physiology and function of autoantigen-specific CD8+ cells from MG/EAMG, and the subset of autoantigen-specific CD8+ cells (Tc1, Tc17, and IL-17+IFN-γ+CD8+ T cells). There are many reports of CD20-expressing T (or CD20 + T) and CXCR5+ CD8 T cells on autoimmune diseases, especially on multiple sclerosis and rheumatoid arthritis. Unfortunately, up to now, there has been no report on these T cells on MG, which might be a good direction for future studies.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

A biphenotypic lymphocyte subset displays both T- and B-cell functionalities

T cell/B cell mixed phenotypic lymphocytes have been observed in different disease contexts, yet their presence and function in physiological conditions remain elusive. Here, we provide evidence for the existence of a lymphocyte subset endogenously expressing both T- and B-cell lineage markers in mice. The majority of these T/B phenotypic lymphocytes (CD3+CD19+) show an origin of pro/pre B cells and distribute widely in mouse bone marrow, lymph nodes, spleen, and peripheral blood. Functional assays show that these biphenotypic lymphocytes can be activated through stimulating TCR or BCR signaling pathways. Moreover, we show that these cells actively participate both the humoral and cellular immune responses elicited by vaccination. Compared to conventional T cells, these biphenotypic lymphocytes can secrete a higher level of IL-2 but a lower level of TNF-α upon antigen specific stimulation. An equivalent lymphocyte subset is found in freshly isolated human PBMCs and exhibits similar functionality, albeit at a lower frequency than in mice.

From helping to regulating – A transcriptomic profile of Ifng+ Il10+ Il21+ Cd4+ Th1 cells indicates their role in regulating inflammation during experimental trypanosomosis

Introduction

Trypanosoma evansi parasite infections cause a chronic animal wasting disease called Surra, and cases of atypical Human Trypanosomosis (aHT). In experimental models, T. evansi infections are hallmarked by the early onset of excessive inflammation. Therefore, balancing the production of inflammatory cytokines by anti-inflammatory IL-10 is crucial for prolonged survival.

Methods

To improve the understanding of trypanosomosis induced immunopathology, we used scRNA-seq data from an experimental chronic T. evansi infection mouse model, resembling natural infection in terms of disease characteristics.

Results and discussion

For the first time, obtained results allowed to assess the transcriptomic profile and heterogeneity of splenic CD4+ T cell subsets, during a trypanosome infection. Here, the predominant subpopulation of T cells was represented by Tbx21(T-bet)+Ccr5+ Id2+ type 1 helper T cells (Th1), followed by Icos+ Cxcr5+Follicular T helper cells (Tfh) and very minor fraction of Il2ra(CD25)+Foxp3+ regulatory T cells (Tregs). Interestingly, the profile of Th1 cells shows that besides Ifng, these cells express high levels of Il10 and Il21, coding for anti-inflammatory and immunoregulatory cytokines. This coincides with the elevated expression of key genes involved in IL-10 and IL-21 secretion pathway such as Stat1 and Stat3, as well as the transcriptional factors Prdm1 (Blimp 1), and Maf (c-Maf). In contrast, there is virtually no IL-10 transcription detected in the Treg population. Finally, differential gene expression and gene ontology analysis of infection-induced Ifng+ Il10+ Il21+ Th1 cells highlights their suppressive function on T cell activation, differentiation and INF-γ production itself. This indicates that during trypanosome infections, the Ifng+ Il10+ Il21+ Th1 cells, rather than Tregs, assume an immune regulatory role that is needed for dampening inflammation.

A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data

MOTIVATION

Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations.

RESULTS

Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles.

AVAILABILITY AND IMPLEMENTATION

The VIMCCA algorithm has been implemented in our toolkit package scbean (≥0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Implementation of Mass Cytometry for Immunoprofiling of Patients with Solid Tumors

Monitoring immune responses to solid cancers may be a better prognostic tool than conventional staging criteria, and it can also serve as an important criterion for the selection of individualized therapy. Multiparametric phenotyping by mass cytometry extended possibilities for immunoprofiling. However, careful optimization of each step of such method is necessary for obtaining reliable results. Also, with respect to procedure length and costs, sample preparation, staining, and storage should be optimized. Here, we designed a panel of 31 antibodies which allows for identification of several subpopulations of lymphoid and myeloid cells in a solid tumor and peripheral blood simultaneously. For sample preparation, disaggregation of tumor tissue with two different collagenases combined with DNase I was compared, and removal of dead or tumor cells by magnetic separation was evaluated. Two possible procedures of barcoding for single-tube staining of several samples were examined. While the palladium-based barcoding affected the stability of several antigens, the staining with two differently labeled CD45 antibodies was suitable for cells isolated from a patient's blood and tumor. The storage of samples in the intercalation solution for up to two weeks did not influence results of the analysis, which allowed the measurement of samples collected within this interval on the same day. This procedure optimized on samples from patients with head and neck squamous cell carcinoma enabled identification of various immune cells including rare subpopulations.

Analysis of Mass Cytometry Data

The CyTOF system produces single cell protein expression data similar to that from flow cytometry, but with an increased number of features measured. Traditionally, analysis of these data is carried out using manual gating, but with the increased dimensionality, manual gating becomes a suboptimal analysis strategy in some cases. To address this, a number of data analysis tools for tasks such as clustering, differential abundance analysis, and visualization have been developed and made freely available. We here introduce some of the more popular tools for CyTOF analysis and exemplify their utility in a common analysis workflow.