Integration of cell-type resolved spatial proteomics and transcriptomics reveals novel mechanisms in early ovarian cancer.

High-grade serous carcinoma (HGSC) is the most common ovarian cancer subtype, typically diagnosed at late stages with poor prognosis. Understanding early molecular events driving HGSC progression is crucial for timely detection and development of effective treatment strategies. We performed and integrated spatial cell-type resolved proteomics and paired transcriptomics across 25 women with precursor lesions of the fallopian tube and/or HGSC.

Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics.

Single-cell transcriptomics (scRNA-seq) has facilitated the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leveraged recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate an scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2500 human CD34+ hematopoietic stem and progenitor cells.

Glycopolymers stabilize protein folding and protein-protein interactions via enthalpic interactions.

Macromolecular crowding is ubiquitous to physiological environments, perturbing the thermodynamics and kinetics of proteins via excluded volume and nonspecific chemical interactions. While crowding has been well-studied and in cells, the inert sugar polymers used to simulate crowding lack the chemical characteristics of biomolecules. Emerging studies guide the development of more relevant models of crowding in the cell, but little work has been done to discern crowding effects on proteins at the cell surface.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression.

Epithelial serous borderline tumors (SBT) are non-invasive neoplastic ovarian lesions that may recur as chemo-resistant low-grade serous cancer (LGSC). While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood. Here, we integrate cell-type resolved spatial proteomics and transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastases in both stroma and tumor.

A β-hairpin peptide derived from Aβ forms different oligomers in the crystal state and in aqueous solution.

The supramolecular assembly of amyloid-β into soluble oligomers is critical Alzheimer's disease (AD) progression. Soluble Aβ oligomers have emerged as neurotoxic species involved in AD progression and some Aβ oligomers are thought to be composed of β-hairpins. In this work, we report the X-ray crystallographic and solution-phase assembly of a macrocyclic β-hairpin peptide that mimics a β-hairpin formed by Aβ.

Feature selection methods affect the performance of scRNA-seq data integration and querying.

The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth.

Squidpy: a scalable framework for spatial omics analysis.

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins.