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.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

The invention(s) cover a composition, where units of the composition are configured to interact with each other (e.g., as neighbors) in order enable decoding of positions of captured target material relative to neighboring units of the composition. In embodiments, the composition includes: a body; and a set of molecules coupled to the body, the set of molecules comprising a first subset and a second subset, wherein the first subset is structured for target analyte capture, and wherein the second subset is structured for interactions with one or more neighboring objects. The invention(s) also cover systems incorporating one or more units of the composition and methods implementing units of the composition.

Identifying proteins and peptides, and more specifically large-scale sequencing of single peptides in a mixture of diverse peptides at the single molecule level is an unmet challenge in the field of protein sequencing. Herein are methods for identifying amino acids in peptides, including peptides comprising unnatural amino acids. In one embodiment, the N-terminal amino acid is labeled with a first label and an internal amino acid is labeled with a second label. In some embodiments, the labels are fluorescent labels. In other embodiments, the internal amino acid is Lysine. In other embodiments, amino acids in peptides are identified based on the fluorescent signature for each peptide at the single molecule level.

Embodiments described herein generally relate to systems and methods for processing mass spectrometry samples. Aspects of the disclosure include systems and methods for processing samples. Additionally, embodiments of the disclosure can also include systems and methods for sample analysis. Various embodiments include data analysis systems and methods for comparing data across samples and sample runs. Data analysis systems can run normalization methods for normalizing raw abundance mass spectrometry data. In some aspects, the normalized data can be used as input for predictive models.

A method for determining in a sample, by mass spectrometry, the amount of one or more analytes selected from the group consisting of N-acetylthreonine, TMAP, phenylacetylglutamine, tryptophan, creatinine, meso-erythritol, arabitol, myo-inositol, N-acetyl serine, N-acetylalanine, 3-methylhistidine, trans-4-hydroxyproline, kynurenine, urea, C-glycosyltryptophan, 3-indoxyl sulfate, pseudouridine, and combinations thereof is described. The method comprises subjecting the sample to an ionization source under conditions suitable to produce one or more ions detectable by mass spectrometry from each of the one or more of the analytes; measuring, by mass spectrometry, the amount of the one or more ions from each of the one or more analytes; and using the measured amount of the one or more ions to determine the amount of each of the one or more analytes in the sample. Also described is a kit comprising one or more isotopically labeled analogues as internal standards for each of the one or more analytes.

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.

A method for identifying in situ presence of a hydrocarbon reservoir or of a pipeline leakage is disclosed. The method can include obtaining a sample from an area of interest, such as a sediment sample or water column sample near a hydrocarbon seep or near an offshore pipeline; analyzing the sample to detect nucleic acid, protein or metabolite signatures that are indicative of cold-shock response; identifying the relative abundance of the cold-shock signatures present in the sample in comparison to the surrounding environment.

Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets, and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

Disclosed herein, inter alia, are compositions and methods of use thereof for interrogating a cell.