The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics.We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics.Utilizing HRM, we profiled acetaminophen (APAP)1-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD).Our findings imply that DIA should be the preferred method for quantitative protein profiling.

SummaryImmune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Single-cell analyses have revealed extensive intra-and inter-patient cancer heterogeneity 1 , but complex single-cell phenotypes and their spatial context are not yet reflected in the histologic stratification that is the foundation of many clinical decisions. Here, we used imaging mass cytometry 2 to simultaneously quantify 35 biomarkers resulting in 720 highdimension immunohistochemistry pathology images of tumor tissue from 352 breast cancer patients for whom long-term survival data were available. Spatial, single-cell analysis identified tumor and stromal single-cell phenotypes, their organization and heterogeneity, and enabled categorization of breast cancer cellular architecture based on cellular composition and tissue organization. Our analysis revealed multi-cellular features of the tumor microenvironment and novel breast cancer subgroups associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intra-tumor phenotypic heterogeneity in a disease-relevant manner with the potential to inform patient-specific diagnosis. MainHistologic and phenotypic differences between tumors guide cancer diagnosis, prognosis, and selection of treatment. Currently, breast cancer patients are graded based on tumor structure and cellular morphology, and subcategorized when more than 1% of tumor cells contain hormone receptors or more than 10% express high levels of HER2 or have amplified HER2 [3][4][5] . This leaves a large portion of cells uncharacterized even though additional molecular subclasses and morphologic features have been identified as prognostic [6][7][8][9] . It is clear that clonal evolution and spatially distinct tumor microenvironments drive inter-and intra-patient cellular