In this work, we find that CD8+ T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49+CD8+ regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8+ T cells efficiently eliminated pathogenic gliadin-specific CD4+ T cells from the leukocytes of celiac disease patients in vitro. We also find elevated levels of KIR+CD8+ T cells, but not CD4+ regulatory T cells, in COVID-19 patients, correlating with disease severity and vasculitis. Selective ablation of Ly49+CD8+ T cells in virus-infected mice led to autoimmunity after infection. Our results indicate that in both species, these regulatory CD8+ T cells act specifically to suppress pathogenic T cells in autoimmune and infectious diseases.

Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are debilitating, clinically heterogeneous and of unknown molecular etiology. A transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period. Distinct gene expression signatures of post-acute sequelae were already present in whole blood during acute infection, with innate and adaptive immune cells implicated in different symptoms. Two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns. In one cluster, sequelae associated with higher expression of immunoglobulin-related genes in an anti-spike antibody titer-dependent manner. In the other, sequelae associated independently of these titers with lower expression of immunoglobulin-related genes, indicating lower non-specific antibody production in individuals with these sequelae. This relationship between lower total immunoglobulins and sequelae was validated in an external cohort. Altogether, multiple etiologies of post-acute sequelae were already detectable during SARS-CoV-2 infection, directly linking these sequelae with the acute host response to the virus and providing early insights into their development.

The discovery and characterization of antigen-specific CD8+ T cell clonotypes typically involves the labor-intensive synthesis and construction of peptide-MHC tetramers. We adapt single-chain trimer (SCT) technologies into a high throughput platform for pMHC library generation, showing that hundreds can be rapidly prepared across multiple Class I HLA alleles. We use this platform to explore the impact of peptide and SCT template mutations on protein expression yield, thermal stability, and functionality. SCT libraries were an efficient tool for identifying T cells recognizing commonly reported viral epitopes. We then construct SCT libraries to capture SARS-CoV-2 specific CD8+ T cells from COVID-19 participants and healthy donors. The immunogenicity of these epitopes is validated by functional assays of T cells with cloned TCRs captured using SCT libraries. These technologies should enable the rapid analyses of peptide-based T cell responses across several contexts, including autoimmunity, cancer, or infectious disease.

T cells engineered to express a tumor-specific αβ T cell receptor (TCR) mediate anti-tumor immunity. However, mispairing of the therapeutic αβ chains with endogenous αβ chains reduces therapeutic TCR surface expression and generates self-reactive TCRs. We report a general strategy to prevent TCR mispairing: swapping constant domains between the α and β chains of a therapeutic TCR. When paired, domain-swapped (ds)TCRs assemble with CD3, express on the cell surface, and mediate antigen-specific T cell responses. By contrast, dsTCR chains mispaired with endogenous chains cannot properly assemble with CD3 or signal, preventing autoimmunity. We validate this approach in cell-based assays and in a mouse model of TCR gene transfer-induced graft-versus-host disease. We also validate a related approach whereby replacement of αβ TCR domains with corresponding γδ TCR domains yields a functional TCR that does not mispair. This work enables the design of safer TCR gene therapies for cancer immunotherapy.

Invariant natural killer T (iNKT) cells are potent immune cells for targeting cancer; however, their clinical application has been hindered by their low numbers in cancer patients. Here, we developed a proof-of-concept for hematopoietic stem cell-engineered iNKT (HSC-iNKT) cell therapy with the potential to provide therapeutic levels of iNKT cells for a patient's lifetime. Using a human HSC engrafted mouse model and a human iNKT TCR gene engineering approach, we demonstrated the efficient and long-term generation of HSC-iNKT cells in vivo. These HSC-iNKT cells closely resembled endogenous human iNKT cells, could deploy multiple mechanisms to attack tumor cells, and effectively suppressed tumor growth in vivo in multiple human tumor xenograft mouse models. Preclinical safety studies showed no toxicity or tumorigenicity of the HSC-iNKT cell therapy. Collectively, these results demonstrated the feasibility, safety, and cancer therapy potential of the proposed HSC-iNKT cell therapy and laid a foundation for future clinical development.

Phenotypic plasticity is associated with non-genetic drug tolerance in several cancers. Such plasticity can arise from chromatin remodeling, transcriptomic reprogramming, and/or protein signaling rewiring, and is characterized as a cell state transition in response to molecular or physical perturbations. This, in turn, can confound interpretations of drug responses and resistance development. Using BRAF-mutant melanoma cell lines as the prototype, we report on a joint theoretical and experimental investigation of the cell-state transition dynamics associated with BRAF inhibitor drug tolerance. Thermodynamically motivated surprisal analysis of transcriptome data was used to treat the cell population as an entropy maximizing system under the influence of time-dependent constraints. This permits the extraction of an epigenetic potential landscape for drug-induced phenotypic evolution. Single-cell flow cytometry data of the same system were modeled with a modified Fokker-Planck-type kinetic model. The two approaches yield a consistent picture that accounts for the phenotypic heterogeneity observed over the course of drug tolerance development. The results reveal that, in certain plastic cancers, the population heterogeneity and evolution of cell phenotypes may be understood by accounting for the competing interactions of the epigenetic potential landscape and state-dependent cell proliferation. Accounting for such competition permits accurate, experimentally verifiable predictions that can potentially guide the design of effective treatment strategies.

CD8+ T cells recognize and eliminate tumors in an antigen-specific manner. Despite progress in characterizing the antitumor T cell repertoire and function, the identification of target antigens remains a challenge. Here we describe the use of chimeric receptors called signaling and antigen-presenting bifunctional receptors (SABRs) in a cell-based platform for T cell receptor (TCR) antigen discovery. SABRs present an extracellular complex comprising a peptide and major histocompatibility complex (MHC), and induce intracellular signaling via a TCR-like signal after binding with a cognate TCR. We devised a strategy for antigen discovery using SABR libraries to screen thousands of antigenic epitopes. We validated this platform by identifying the targets recognized by public TCRs of known specificities. Moreover, we extended this approach for personalized neoantigen discovery.

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness.

TCR-signaling strength generally correlates with peptide-MHC binding affinity; however, exceptions exist. We find high-affinity, yet non-stimulatory, interactions occur with high frequency in the human T cell repertoire. Here, we studied human TCRs that are refractory to activation by pMHC ligands despite robust binding. Analysis of 3D affinity, 2D dwell time, and crystal structures of stimulatory versus non-stimulatory TCR-pMHC interactions failed to account for their different signaling outcomes. Using yeast pMHC display, we identified peptide agonists of a formerly non-responsive TCR. Single-molecule force measurements demonstrated the emergence of catch bonds in the activating TCR-pMHC interactions, correlating with exclusion of CD45 from the TCR-APC contact site. Molecular dynamics simulations of TCR-pMHC disengagement distinguished agonist from non-agonist ligands based on the acquisition of catch bonds within the TCR-pMHC interface. The isolation of catch bonds as a parameter mediating the coupling of TCR binding and signaling has important implications for TCR and antigen engineering for immunotherapy.

Local immune activation at mucosal surfaces, mediated by mucosal lymphoid tissues, is vital for effective immune responses against pathogens. While pathogens like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread to multiple organs, patients with coronavirus disease 2019 (COVID-19) primarily experience inflammation and damage in their lungs. To investigate this apparent organ-specific immune response, we develop an analytical framework that recognizes the significance of mucosal lymphoid tissues. This framework combines histology, immunofluorescence, spatial transcript profiling, and mathematical modeling to identify cellular and gene expression differences between the lymphoid tissues of the lung and the gut and predict the determinants of those differences. Our findings indicate that mucosal lymphoid tissues are pivotal in organ-specific immune response to SARS-CoV-2, mediating local inflammation and tissue damage and contributing to immune dysfunction. The framework developed here has potential utility in the study of long COVID and may streamline biomarker discovery and treatment design for diseases with differential pathologies at the organ level.

We report on peptide-based ligands matured through the protein catalyzed capture (PCC) agent method to tailor molecular binders for in vitro sensing/diagnostics and in vivo pharmacokinetics parameters. A vascular endothelial growth factor (VEGF) binding peptide and a peptide against the protective antigen (PA) protein of Bacillus anthracis discovered through phage and bacterial display panning technologies, respectively, were modified with click handles and subjected to iterative in situ click chemistry screens using synthetic peptide libraries. Each azide-alkyne cycloaddition iteration, promoted by the respective target proteins, yielded improvements in metrics for the application of interest. The anti-VEGF PCC was explored as a stable in vivo imaging probe. It exhibited excellent stability against proteases and a mean elimination in vivo half-life (T1/2 ) of 36 min. Intraperitoneal injection of the reagent results in slow clearance from the peritoneal cavity and kidney retention at extended times, while intravenous injection translates to rapid renal clearance. The ligand competed with the commercial antibody for binding to VEGF in vivo. The anti-PA ligand was developed for detection assays that perform in demanding physical environments. The matured anti-PA PCC exhibited no solution aggregation, no fragmentation when heated to 100°C, and  > 81% binding activity for PA after heating at 90°C for 1 h. We discuss the potential of the PCC agent screening process for the discovery and enrichment of next generation antibody alternatives.

As the tissue that contains the largest representation of the human proteome, blood is the most important fluid for clinical diagnostics. However, although changes of plasma protein profiles reflect physiological or pathological conditions associated with many human diseases, only a handful of plasma proteins are routinely used in clinical tests. Reasons for this include the intrinsic complexity of the plasma proteome, the heterogeneity of human diseases and the rapid degradation of proteins in sampled blood. We report an integrated microfluidic system, the integrated blood barcode chip that can sensitively sample a large panel of protein biomarkers over broad concentration ranges and within 10 min of sample collection. It enables on-chip blood separation and rapid measurement of a panel of plasma proteins from quantities of whole blood as small as those obtained by a finger prick. Our device holds potential for inexpensive, noninvasive and informative clinical diagnoses, particularly in point-of-care settings.

Cross-reactivity and direct killing of target cells remain underexplored for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-specific CD8+ T cells. Isolation of T cell receptors (TCRs) and overexpression in allogeneic cells allows for extensive T cell reactivity profiling. We identify SARS-CoV-2 RNA-dependent RNA polymerase (RdRp/NSP12) as highly conserved, likely due to its critical role in the virus life cycle. We perform single-cell TCRαβ sequencing in human leukocyte antigen (HLA)-A∗02:01-restricted, RdRp-specific T cells from SARS-CoV-2-unexposed individuals. Human T cells expressing these TCRαβ constructs kill target cell lines engineered to express full-length RdRp. Three TCR constructs recognize homologous epitopes from common cold coronaviruses, indicating CD8+ T cells can recognize evolutionarily diverse coronaviruses. Analysis of individual TCR clones may help define vaccine epitopes that can induce long-term immunity against SARS-CoV-2 and other coronaviruses.

Human adaptive-like natural killer (NK) cells express low levels of FcεRIγ (FcRγ-/low) and are reported to accumulate during COVID-19 infection; however, the mechanism underlying and regulating FcRγ expression in NK cells has yet to be fully defined. We observed lower FcRγ protein expression in NK cell subsets from lung transplant patients during rapamycin treatment, suggesting a link with reduced mTOR activity. Further, FcRγ-/low NK cell subsets from healthy donors displayed reduced mTOR activity. We discovered that FcRγ upregulation is dependent on cell proliferation progression mediated by IL-2, IL-15, or IL-12, is sensitive to mTOR suppression, and is inhibited by TGFβ or IFNα. Accordingly, the accumulation of adaptive-like FcRγ-/low NK cells in COVID-19 patients corresponded to increased TGFβ and IFNα levels and disease severity. Our results show that an adaptive-like NK cell phenotype is induced by diminished cell proliferation and has an early prognostic value for increased TGFβ and IFNα levels in COVID-19 infection associated with disease severity.

The response of patients with recurrent glioblastoma multiforme to neoadjuvant immune checkpoint blockade has been challenging to interpret due to the inter-patient and intra-tumor heterogeneity. We report on a comparative analysis of tumor tissues collected from patients with recurrent glioblastoma and high-risk melanoma, both treated with neoadjuvant checkpoint blockade. We develop a framework that uses multiplex spatial protein profiling, machine learning-based image analysis, and data-driven computational models to investigate the pathophysiological and molecular factors within the tumor microenvironment that influence treatment response. Using melanoma to guide the interpretation of glioblastoma analyses, we interrogate the protein expression in microscopic compartments of tumors, and determine the correlates of cytotoxic CD8+ T cells, tumor growth, treatment response, and immune cell-cell interaction. This work reveals similarities shared between glioblastoma and melanoma, immunosuppressive factors that are unique to the glioblastoma microenvironment, and potential co-targets for enhancing the efficacy of neoadjuvant immune checkpoint blockade.

We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 135,000 high-confidence SARS-CoV-2-specific TCRs. This database is made freely available, and the data contained in it can be downloaded and analyzed online or offline to assist with the global efforts to understand the immune response to the SARS-CoV-2 virus and develop new interventions.

Neoantigen-specific T cells are increasingly viewed as important immunotherapy effectors, but physically isolating these rare cell populations is challenging. Here, we describe a sensitive method for the enumeration and isolation of neoantigen-specific CD8+ T cells from small samples of patient tumor or blood. The method relies on magnetic nanoparticles that present neoantigen-loaded major histocompatibility complex (MHC) tetramers at high avidity by barcoded DNA linkers. The magnetic particles provide a convenient handle to isolate the desired cell populations, and the barcoded DNA enables multiplexed analysis. The method exhibits superior recovery of antigen-specific T cell populations relative to literature approaches. We applied the method to profile neoantigen-specific T cell populations in the tumor and blood of patients with metastatic melanoma over the course of anti-PD1 checkpoint inhibitor therapy. We show that the method has value for monitoring clinical responses to cancer immunotherapy and might help guide the development of personalized mutational neoantigen-specific T cell therapies and cancer vaccines.

Antibiotic resistant infections are projected to cause over 10 million deaths by 2050, yet the development of new antibiotics has slowed. This points to an urgent need for methodologies for the rapid development of antibiotics against emerging drug resistant pathogens. We report on a generalizable combined computational and synthetic approach, called antibody-recruiting protein-catalyzed capture agents (AR-PCCs), to address this challenge. We applied the combinatorial protein catalyzed capture agent (PCC) technology to identify macrocyclic peptide ligands against highly conserved surface protein epitopes of carbapenem-resistant Klebsiella pneumoniae, an opportunistic Gram-negative pathogen with drug resistant strains. Multi-omic data combined with bioinformatic analyses identified epitopes of the highly expressed MrkA surface protein of K. pneumoniae for targeting in PCC screens. The top-performing ligand exhibited high-affinity (EC50 ∼50 nM) to full-length MrkA, and selectively bound to MrkA-expressing K. pneumoniae, but not to other pathogenic bacterial species. AR-PCCs that bear a hapten moiety promoted antibody recruitment to K. pneumoniae, leading to enhanced phagocytosis and phagocytic killing by macrophages. The rapid development of this highly targeted antibiotic implies that the integrated computational and synthetic toolkit described here can be used for the accelerated production of antibiotics against drug resistant bacteria.