Gas-Phase Fractionation Data-Independent Acquisition Analysis of 3D Cocultured Spheroid Tumor Model Reveals Altered Translational Processes and Signaling Using Proteomics.

Colorectal cancer (CRC) contains considerable heterogeneity; therefore, models of the disease must also reflect the multifarious components. Compared to traditional 2D models, 3D cellular models, such as tumor spheroids, have the utility to determine the drug efficacy of potential therapeutics. Monoculture spheroids are well-known to recapitulate gene expression, cell signaling, and pathophysiological gradients of avascularized tumors. However, they fail to mimic the stromal cell influence present in CRC, which is known to perturb drug efficacy and is associated with metastatic, late-stage colorectal cancer. This study seeks to develop a cocultured spheroid model using carcinoma and noncancerous fibroblast cells. We characterized the proteomic profile of cocultured spheroids in comparison to monocultured spheroids using data-independent acquisition with gas-phase fractionation. Specifically, we determined that proteomic differences related to translation and mTOR signaling are significantly increased in cocultured spheroids compared to monocultured spheroids. Proteins related to fibroblast function, such as exocytosis of coated vesicles and secretion of growth factors, were significantly differentially expressed in the cocultured spheroids. Finally, we compared the proteomic profiles of both the monocultured and cocultured spheroids against a publicly available data set derived from solid CRC tumors. We found that the proteome of the cocultured spheroids more closely resembles that of the patient samples, indicating their potential as tumor mimics.

Scribe: Next Generation Library Searching for DDA Experiments.

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

Co-culturing multicellular tumor models: Modeling the tumor microenvironment and analysis techniques.

Advances in two-dimensional (2D) and three-dimensional (3D) cell culture over the last 10 years have led to the development of a plethora of methods for cultivating tumor models. More recently, cellular co-cultures have become a suitable testbed. The first portion of this review focuses on co-culturing methods that have been developed in recent years utilizing the multicellular tumor spheroid model. The latter portion describes techniques that are used to analyze the proteomes of mono- or co-cultured tumor models, with a focus on mass spectrometry (MS)-based analyses. Protein profiles are important indicators of the tumor heterogeneity. Therefore, there is a specific focus within this review on analysis by MS and MS imaging methods evaluating the proteomic profiles of 2D and 3D co-cultures. While these models are incredibly important for biological research, so far, they have not been widely explored on the proteomic level. With this review, we aim to introduce these systems to an analytical audience, with the goal of highlighting MS as an underutilized tool for proteomic analysis of tumor models.

A workflow for targeted proteomics assay development using a versatile linear ion trap.

Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopeDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications.

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.