Oligonucleotide-directed proximity-interactome mapping (O-MAP): A unified method for discovering RNA-interacting proteins, transcripts and genomic loci in situ.

Throughout biology, RNA molecules form complex networks of molecular interactions that are central to their function, but remain challenging to investigate. Here, we introduce Oligonucleotide-mediated proximity-interactome MAPping (O-MAP), a straightforward method for elucidating the biomolecules near an RNA of interest, within its native cellular context. O-MAP uses programmable oligonucleotide probes to deliver proximity-biotinylating enzymes to a target RNA, enabling nearby molecules to be enriched by streptavidin pulldown. O-MAP induces exceptionally precise RNA-localized in situ biotinylation, and unlike alternative methods it enables straightforward optimization of its targeting accuracy. Using the 47S pre-ribosomal RNA and long noncoding RNA Xist as models, we develop O-MAP workflows for unbiased discovery of RNA-proximal proteins, transcripts, and genomic loci. This revealed unexpected co-compartmentalization of Xist and other chromatin-regulatory RNAs and enabled systematic characterization of nucleolar-chromatin interactions across multiple cell lines. O-MAP is portable to cultured cells, organoids, and tissues, and to RNAs of various lengths, abundances, and sequence composition. And, O-MAP requires no genetic manipulation and uses exclusively off-the-shelf parts. We therefore anticipate its application to a broad array of RNA phenomena.

Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

Systematic discovery and validation of T cell targets directed against oncogenic KRAS mutations.

Oncogenic mutations in KRAS can be recognized by T cells on specific class I human leukocyte antigen (HLA-I) molecules, leading to tumor control. To date, the discovery of T cell targets from KRAS mutations has relied on occasional T cell responses in patient samples or the use of transgenic mice. To overcome these limitations, we have developed a systematic target discovery and validation pipeline. We evaluate the presentation of mutant KRAS peptides on individual HLA-I molecules using targeted mass spectrometry and identify 13 unpublished KRASG12C/D/R/V mutation/HLA-I pairs and nine previously described pairs. We assess immunogenicity, generating T cell responses to nearly all targets. Using cytotoxicity assays, we demonstrate that KRAS-specific T cells and T cell receptors specifically recognize endogenous KRAS mutations. The discovery and validation of T cell targets from KRAS mutations demonstrate the potential for this pipeline to aid the development of immunotherapies for important cancer targets.

Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction.

Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.