Provided herein are methods and systems for proteome analysis that are at least partially automated and/or performed robotically. In some aspects, the methods and systems described herein can rapidly and efficiently provide protein identification of each of the proteins from a proteome, or a complement of proteins, obtained from extremely small amounts of biological samples. The identified proteins can be imaged quantitatively over a spatial region. Automation and robotics facilitates the throughput of the methods and systems, which enables protein imaging and/or rapid proteome analysis.

The present invention generally provides, in various embodiments, methods of analyzing samples having a low abundance of proteins, e.g., single cells, utilizing liquid chromatography and tandem mass spectroscopy (LC-MS/MS).

Provided herein are methods for identifying modulators of cell surface protein interactions and activities, as well as modulators of cell surface protein interactions and activities.

The disclosure describes a comprehensive metabolome analysis of urine samples that identified panels of metabolite markers for diagnosis and monitoring of alloimmune injury, acute rejection, and BK virus nephropathy. The disclosure provides non-invasive ways to monitor the status of transplanted kidneys by monitoring the presence of defined metabolite panels over a period of time. The metabolite panels of the disclosure can distinguish the between kidney injuries of distinct etiology with high sensitivity and specificity.

Systems and methods are presented that allow for predicting treatment response of a tumor to a checkpoint inhibitor. In one exemplary aspect, the treatment response is directly associated with a relatively high number of patient- and tumor-specific immunologically visible neoepitopes. Specific mutational patterns in the nucleic acid encoding the neoepitope may be further indicative of treatment response.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed. The method comprises: using a first device to generate smoke, aerosol or vapour from a target comprising or consisting of a microbial population; mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and analysing said spectrometric data in order to analyse said microbial population.

Various embodiments provide compositions and methods for detecting cancers containing an NRG1 fusion event and treating a patient with a therapeutic agent that is targeted to the NRG1 fusion. Exemplary compositions for treating cancers containing the NRG1 fusion may comprise therapeutic agents inhibiting Epidermal Growth Factor Receptor and/or ERBB2 such as cetuximab, panitumumab, Sym004, MM-151, mAb 806, mAb 528, MEHD794A, gefitinib, erlotinib, lapatinib, afatinib, PD153035, AG1478, trastuzumab, and pertuzumab. In some embodiments, the therapeutic agent may be a combination of trastuzumab, and pertuzumab.

Methods, systems and circuits evaluate a subject's CVD risk using a risk parameter that includes at least one HDL and inflammatory biomarker interaction parameter. The inflammatory biomarker may optionally comprise NMR derived measurements of GlycA from at least one biosample of the subject. The risk parameter may be gender-specific.

Systems and methods for analyzing metabolite concentration in a subject using a medical imaging system are provided. The method includes, using a nuclear magnetic resonance (NMR] system, acquiring data from a subject during multiple acquisitions using different echo times for the multiple acquisitions to create a chemical shift domain. The method also includes, using the chemical shift domain, identifying metabolites by at least two chemical shifts and generating a report indicating the metabolites.

Multiparametric analysis is performed at the single cell level of biological samples obtained from an individual during pregnancy to obtain a determination of changes in the interactome, integrating metabolome, immunome and proteome features during pregnancy that are predictive of time to onset of labor.