Summary To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single cell analysis of the tumor, non-involved lung and blood cells together with multiplex tissue imaging to assess spatial cell distribution, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies.

An individual's T cell repertoire dynamically encodes their pathogen exposure history. To determine whether pathogen exposure signatures can be identified by documenting public T cell receptors (TCRs), we profiled the T cell repertoire of 666 subjects with known cytomegalovirus (CMV) serostatus by immunosequencing. We developed a statistical classification framework that could diagnose CMV status from the resulting catalog of TCRβ sequences with high specificity and sensitivity in both the original cohort and a validation cohort of 120 different subjects. We also confirmed that three of the identified CMV-associated TCRβ molecules bind CMV in vitro, and, moreover, we used this approach to accurately predict the HLA-A and HLA-B alleles of most subjects in the first cohort. As all memory T cell responses are encoded in the common format of somatic TCR recombination, our approach could potentially be generalized to a wide variety of disease states, as well as other immunological phenotypes, as a highly parallelizable diagnostic strategy.

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Plasmodium falciparum causes the most severe form of malaria and kills up to 2.7 million people annually. Despite the global importance of P. falciparum, the vast majority of its proteins have not been characterized experimentally. Here we identify P. falciparum protein-protein interactions using a high-throughput version of the yeast two-hybrid assay that circumvents the difficulties in expressing P. falciparum proteins in Saccharomyces cerevisiae. From more than 32,000 yeast two-hybrid screens with P. falciparum protein fragments, we identified 2,846 unique interactions, most of which include at least one previously uncharacterized protein. Informatic analyses of network connectivity, coexpression of the genes encoding interacting fragments, and enrichment of specific protein domains or Gene Ontology annotations were used to identify groups of interacting proteins, including one implicated in chromatin modification, transcription, messenger RNA stability and ubiquitination, and another implicated in the invasion of host cells. These data constitute the first extensive description of the protein interaction network for this important human pathogen.