Postmortem high-dimensional immune profiling of severe COVID-19 patients reveals distinct patterns of immunosuppression and immunoactivation

A complete diagnostic autopsy is the gold-standard to gain insight into Coronavirus disease 2019 (COVID-19) pathogenesis. To delineate the in situ immune responses to SARS-CoV-2 viral infection, here we perform comprehensive high-dimensional transcriptional and spatial immune profiling in 22 COVID-19 decedents from Wuhan, China. We find TIM-3-mediated and PD-1-mediated immunosuppression as a hallmark of severe COVID-19, particularly in men, with PD-1+ cells being proximal rather than distal to TIM-3+ cells. Concurrently, lymphocytes are distal, while activated myeloid cells are proximal, to SARS-CoV-2 viral antigens, consistent with prevalent SARS-CoV-2 infection of myeloid cells in multiple organs. Finally, viral load positively correlates with specific immunosuppression and dendritic cell markers. In summary, our data show that SARS-CoV-2 viral infection induces lymphocyte suppression yet myeloid activation in severe COVID-19, so these two cell types likely have distinct functions in severe COVID-19 disease progression, and should be targeted differently for therapy.

A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer

The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy.

Simultaneous analysis of antigen-specific B and T cells after SARS-CoV-2 infection and vaccination

Conventional methods for quantifying and phenotyping antigen-specific lymphocytes can rapidly deplete irreplaceable specimens. This is due to the fact that antigen-specific T and B cells have historically been analyzed in independent assays each requiring millions of cells. A technique that facilitates the simultaneous detection of antigen-specific T and B cells would allow for more thorough immune profiling with significantly reduced sample requirements. To this end, we developed the B And T cell Tandem Lymphocyte Evaluation (BATTLE) assay, which allows for the simultaneous identification of SARS-CoV-2 Spike reactive T and B cells using an optimized Activation Induced Marker (AIM) T cell assay and dual-color B cell antigen probes. Using this assay, we demonstrate that antigen-specific B and T cell subsets can be identified simultaneously using conventional flow cytometry platforms and provide insight into the differential effects of mRNA vaccination on B and T cell populations following natural SARS-CoV-2 infection.

Systems immune profiling of variant-specific vaccination against SARS-CoV-2

Lipid-nanoparticle(LNP)-mRNA vaccines offer protection against COVID-19. However, multiple variant lineages caused widespread breakthrough infections. There is no report on variant-specific vaccines to date. Here, we generated LNP-mRNAs specifically encoding wildtype, B.1.351 and B.1.617 SARS-CoV-2 spikes, and systematically studied their immune responses in animal models. All three LNP-mRNAs induced potent antibody responses in mice. However, WT-LNP-mRNA vaccination showed reduced neutralization against B.1.351 and B.1.617; and B.1.617-specific vaccination showed differential neutralization. All three vaccine candidates elicited antigen-specific CD8 and CD4 T cell responses. Single cell transcriptomics of B.1.351-LNP-mRNA and B.1.617-LNP-mRNA vaccinated animals revealed a systematic landscape of immune cell populations and global gene expression. Variant-specific vaccination induced a systemic increase in reactive CD8 T cell population, with a strong signature of transcriptional and translational machineries in lymphocytes. BCR-seq and TCR-seq unveiled repertoire diversity and clonal expansions in vaccinated animals. These data provide direct systems immune profiling of variant-specific LNP-mRNA vaccination in vivo.

AImmune: a new blood-based machine learning approach to improving immune profiling analysis on COVID-19 patients

A massive number of transcriptomic profiles of blood samples from COVID-19 patients has been produced since pandemic COVID-19 begins, however, these big data from primary studies have not been well integrated by machine learning approaches. Taking advantage of modern machine learning arthrograms, we integrated and collected single cell RNA-seq (scRNA-seq) data from three independent studies, identified genes potentially available for interpretation of severity, and developed a high-performance deep learning-based deconvolution model AImmune that can predict the proportion of seven different immune cells from the bulk RNA-seq results of human peripheral mononuclear cells. This novel approach which can be used for clinical blood testing of COVID-19 on the ground that previous research shows that mRNA alternations in blood-derived PBMCs may serve as a severity indicator. Assessed on real-world data sets, the AImmune model outperformed the most recognized immune profiling model CIBERSORTx. The presented study showed the results obtained by the true scRNA-seq route can be consistently reproduced through the new approach AImmune, indicating a potential replacing the costly scRNA-seq technique for the analysis of circulating blood cells for both clinical and research purposes.

Tumor-immune profiling of CT-26 and Colon 26 syngeneic mouse models reveals mechanism of anti-PD-1 response

Background Immune checkpoint blockade (ICB) therapies have changed the paradigm of cancer therapies. However, anti-tumor response of the ICB is insufficient for many patients and limited to specific tumor types. Despite many preclinical and clinical studies to understand the mechanism of anti-tumor efficacy of ICB, the mechanism is not completely understood. Harnessing preclinical tumor models is one way to understand the mechanism of treatment response. Methods In order to delineate the mechanisms of anti-tumor activity of ICB in preclinical syngeneic tumor models, we selected two syngeneic murine colorectal cancer models based on in vivo screening for sensitivity with anti-PD-1 therapy. We performed tumor-immune profiling of the two models to identify the potential mechanism for anti-PD-1 response. Results We performed in vivo screening for anti-PD-1 therapy across 23 syngeneic tumor models and found that CT-26 and Colon 26, which are murine colorectal carcinoma derived from BALB/c mice, showed different sensitivity to anti-PD-1. CT-26 tumor mice were more sensitive to the anti-PD-1 antibody than Colon 26, while both models show similarly sensitivity to anti-CTLA4 antibody. Immune-profiling showed that CT-26 tumor tissue was infiltrated with more immune cells than Colon 26. Genomic/transcriptomic analyses highlighted thatWnt pathway was one of the potential differences between CT-26 and Colon 26, showing Wnt activity was higher in Colon 26 than CT-26. . Conclusions CT-26 and Colon 26 syngeneic tumor models showed different sensitivity to anti-PD-1 therapy, although both tumor cells are murine colorectal carcinoma cell lines from BALB/c strain. By characterizing the mouse cells lines and tumor-immune context in the tumor tissues with comprehensive analysis approaches, we found that CT-26 showed “hot tumor” profile with more infiltrated immune cells than Colon 26. Further pathway analyses enable us to propose a hypothesis that Wnt pathway could be one of the major factors to differentiate CT-26 from Colon 26 model and link to anti-PD-1 response. Our approach to focus on preclinical tumor models with similar genetic background but different sensitivity to anti-PD-1 therapy would contribute to illustrating the potential mechanism of anti-PD-1 response and to generating a novel concept to synergize current anti-PD-1 therapies for cancer patients.