Tissue-specific features of the T cell repertoire after allogeneic hematopoietic cell transplantation in human and mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5 to 18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, and in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleens in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end stage of an inflammatory disorder after lymphocyte-directed therapy. These findings also underscore the importance of studying T cell in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and posttransplant T cell landscape.

High-throughput interrogation of immune responses using the Human Immune Profiling Pipeline

The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1.

Impaired humoral immunity is associated with prolonged COVID-19 despite robust CD8 T cell responses

How immune dysregulation affects recovery from COVID-19 infection in patients with cancer remains unclear. We analyzed cellular and humoral immune responses in 103 patients with prior COVID-19 infection, more than 20% of whom had delayed viral clearance. Delayed clearance was associated with loss of antibodies to nucleocapsid and spike proteins with a compensatory increase in functional T cell responses. High-dimensional analysis of peripheral blood samples demonstrated increased CD8⁺ effector T cell differentiation and a broad but poorly converged COVID-specific T cell receptor (TCR) repertoire in patients with prolonged disease. Conversely, patients with a CD4⁺ dominant immunophenotype had a lower incidence of prolonged disease and exhibited a deep and highly select COVID-associated TCR repertoire, consistent with effective viral clearance and development of T cell memory. These results highlight the importance of B cells and CD4⁺ T cells in promoting durable SARS-CoV-2 clearance and the significance of coordinated cellular and humoral immunity for long-term disease control.

Pathway analysis of genomic pathology tests for prognostic cancer subtyping

Genomic test results collected during the provision of medical care and stored in Electronic Health Record (EHR) systems represent an opportunity for clinical research into disease heterogeneity and clinical outcomes. In this paper, we evaluate the use of genomic test reports ordered for cancer patients in order to derive cancer subtypes and to identify biological pathways predictive of poor survival outcomes. A novel method is proposed to calculate patient similarity based on affected biological pathways rather than gene mutations. We demonstrate that this approach identifies subtypes of prognostic value and biological pathways linked to survival, with implications for precision treatment selection and a better understanding of the underlying disease. We also share lessons learned regarding the opportunities and challenges of secondary use of observational genomic data to conduct such research.

SNOMEDtxt: Natural Language Generation from SNOMED Ontology

SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descriptions using Natural Language Generation techniques. Generated text is evaluated using both computational methods and clinician and lay user feedback. User evaluations indicate that lay people prefer generated text to the original SNOMED content, find it more informative, and understand it significantly better. This method promises to improve access to clinical knowledge for patients and the medical community and to assist in ontology auditing through natural language descriptions.