A concentrated sample having enhanced concentration of the one or more different populations of target rare molecules is incubated with, for each different population of target rare molecules, a particulate or non-particulate affinity agent that comprises a specific binding partner that is specific for and binds to a target rare molecule. The affinity agent comprises a mass spectrometry (MS) label precursor or a first alteration agent, which either facilitates the formation of an MS label from the MS label precursor or releases an entity that comprises the MS label precursor from the affinity agent. The MS label corresponds to one of the populations of target rare molecules. A second alteration agent is employed if the first alteration agent does not facilitate the formation of an MS label from the MS label precursor. MS analysis is used to determine each different MS label.

The present invention relates to peptides, proteins, nucleic acids and cells for use in immunotherapeutic methods. In particular, the present invention relates to the immunotherapy of cancer. The present invention furthermore relates to tumor-associated T-cell peptide epitopes, alone or in combination with other tumor-associated peptides that can for example serve as active pharmaceutical ingredients of vaccine compositions that stimulate anti-tumor immune responses, or to stimulate T cells ex vivo and transfer into patients. Peptides bound to molecules of the major histocompatibility complex (MHC), or peptides as such, can also be targets of antibodies, soluble T-cell receptors, and other binding molecules.

The present invention relates to methods for diagnosing diseases and disorders by measuring the amounts of one or more compounds in lamellar bodies isolated from a sample derived from a subject suspected of suffering from a disorder. Also disclosed are methods for monitoring efficacy of a treatment, methods for monitoring disease progression in a subject, as well as computer-implemented methods for diagnosis and systems for performing said methods.

The present invention provides assays and assay systems for use in spatially encoded biological assays. The invention provides an assay system comprising an assay capable of high levels of multiplexing where reagents are provided to a biological sample in defined spatial patterns; instrumentation capable of controlled delivery of reagents according to the spatial patterns; and a decoding scheme providing a readout that is digital in nature.

The disclosure relates to a method which is suitable for the quality control and signal correction of mass spectrometry data of biological tissue samples and is based on the analysis of the chemical background signal observed in a spectrum. It exploits the fact that the chemical background signal contains components from a plurality of polymer molecules, whose chemical structure has strong regularities. These regularities mean that the observed masses are subject to certain statistical distributions, which are each characteristic of the class of molecule. By analyzing these statistical properties, it is possible to detect and correct any mass shifts which may be present.

The present invention relates to a mass spectrometric method for determining isomeric amino acid residues of a peptide and comprises the steps of: generating a list of peptide candidates using the amino acid lead sequence of the peptide, said lead sequence comprising positions of determined amino acid residues and at least one position with undetermined isomeric amino acid residues; predicting the collision cross section for each peptide candidate; comparing the predicted collision cross section of each peptide candidate with an experimentally determined collision cross section of the peptide and assigning the isomeric amino acid residues of the best matching peptide candidate to the isomeric amino acid residues of the peptide.

The present systems and methods are directed to de novo identification of peptide sequences from tandem mass spectrometry data. The systems and methods uses unconverted mass spectrometry data from which features are extracted. Using unconverted mass spectrometry data reduces the loss of information and provides more accurate sequencing of peptides. The systems and methods combine deep learning and neural networks to sequencing of peptides.

The present invention relates to peptides, proteins, nucleic acids and cells for use in immunotherapeutic methods. In particular, the present invention relates to the immunotherapy of cancer. The present invention furthermore relates to tumor-associated T-cell peptide epitopes, alone or in combination with other tumor-associated peptides that can for example serve as active pharmaceutical ingredients of vaccine compositions that stimulate anti-tumor immune responses, or to stimulate T cells ex vivo and transfer into patients. Peptides bound to molecules of the major histocompatibility complex (MHC), or peptides as such, can also be targets of antibodies, soluble T-cell receptors, and other binding molecules.

A laser ablation cell (1) comprises a flow channel (11) having an essentially constant cross-sectional area so as to ensure a strictly laminar flow in the flow channel. A sample chamber (21) is provided adjacent to a lateral opening (14) of the flow channel. A laser beam (41) enters the sample chamber (21) through a lateral window (16) and impinges on a surface (24) of a sample (23) to ablate material from the sample. The sample may be positioned in such a distance from the flow channel that the laser-generated aerosol mass distribution has its center within the flow channel. This leads to short aerosol washout times. The laser ablation cell is particularly well suited for aerosol generation in inductively coupled plasma mass spectrometry (ICPMS), including imaging applications.

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).