Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

T cell receptor-centric perspective to multimodal single-cell data analysis

The T cell receptor (TCR), despite its importance, is underutilized in single-cell analysis, with gene expression features solely driving current strategies. Here, we argue for a TCR-first approach, more suited toward T cell repertoires. To this end, we curated a large T cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head and neck cancer, blood cancer, and lung transplantation. Here, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We showcase the utility of a TCR-first approach through application of the STEGO.R tool for the identification of treatment-related dynamics and previously unknown public T cell clusters with potential antigen-specific properties. Thus, the paradigm shift to a TCR-first can highlight overlooked key T cell features that have the potential for improvements in immunotherapy and diagnostics.

Immunologic responses to the third and fourth doses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in cell therapy recipients: a systematic review and meta-analysis

BACKGROUND

Multiple studies have provided evidence of suboptimal or poor immune responses to SARS-CoV-2 vaccines in recipients of hematopoietic stem cell transplantation (HSCT) and chimeric antigen receptor-T (CAR-T) cell therapy compared to healthy individuals. Given the dynamic nature of SARS-CoV2, characterized by the emergence of many viral variations throughout the general population, there is ongoing discussion regarding the optimal quantity and frequency of additional doses required to sustain protection against SARS-CoV2 especially in this susceptible population. This systematic review and meta-analysis investigated the immune responses of HSCT and CAR-T cell therapy recipients to additional doses of the SARS-CoV-2 vaccines.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study involved a comprehensive search across PubMed, Scopus, Web of Science Core Collection, Embase, and Cochrane Biorxiv and medRxiv, focusing on the serological responses to the third and fourth vaccine doses in HSCT and CAR-T cell patients.

RESULTS

This study included 32 papers, with 31 qualifying for the meta-analysis. Results showed that after the third dose, the seroconversion rate in HSCT and CAR-T cell therapy recipients who didn't respond to the second dose was 46.10 and 17.26%, respectively. Following the fourth dose, HSCT patients had a seroconversion rate of 27.23%. Moreover, post-third-dose seropositivity rates were 87.14% for HSCT and 32.96% for CAR-T cell therapy recipients. Additionally, the seropositive response to the fourth dose in the HSCT group was 90.04%.

CONCLUSION

While a significant portion of HSCT recipients developed antibodies after additional vaccinations, only a minority of CAR-T cell therapy patients showed a similar response. This suggests that alternative vaccination strategies are needed to protect these vulnerable groups effectively. Moreover, few studies have reported cellular responses to additional SARS-CoV-2 vaccinations in these patients. Further studies evaluating cellular responses are required to determine a more precise assessment of immunogenicity strength against SARS-CoV-2 after additional doses.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Uterine macrophages and NK cells exhibit population and gene-level changes after implantation but maintain pro-invasive properties

Introduction

Prior to pregnancy, hormonal changes lead to cellular adaptations in the endometrium allowing for embryo implantation. Critical for successful pregnancy establishment, innate immune cells constitute a significant proportion of uterine cells prior to arrival of the embryo and throughout the first trimester in humans and animal models. Abnormal uterine immune cell function during implantation is believed to play a role in multiple adverse pregnancy outcomes. Current work in humans has focused on uterine immune cells present after pregnancy establishment, and limited in vitro models exist to explore unique functions of these cells.

Methods

With single-cell RNA-sequencing (scRNAseq), we comprehensively compared the human uterine immune landscape of the endometrium during the window of implantation and the decidua during the first trimester of pregnancy.

Results

We uncovered global and cell-type-specific gene signatures for each timepoint. Immune cells in the endometrium prior to implantation expressed genes associated with immune metabolism, division, and activation. In contrast, we observed widespread interferon signaling during the first trimester of pregnancy. We also provide evidence of specific inflammatory pathways enriched in pre- and post-implantation macrophages and natural killer (NK) cells in the uterine lining. Using our novel implantation-on-a-chip (IOC) to model human implantation ex vivo, we demonstrate for the first time that uterine macrophages strongly promote invasion of extravillous trophoblasts (EVTs), a process essential for pregnancy establishment. Pre- and post-implantation uterine macrophages promoted EVT invasion to a similar degree as pre- and post-implantation NK cells on the IOC.

Conclusions

This work provides a foundation for further investigation of the individual roles of uterine immune cell subtypes present prior to embryo implantation and during early pregnancy, which will be critical for our understanding of pregnancy complications associated with abnormal trophoblast invasion and placentation.

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.