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Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound. Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.

A brief overview of stem cell-derived organoids: how they are made and what the challenges are.

The second, genome-wide phase of the Encyclopedia of DNA Elements (ENCODE) project is being reported.

Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future. Cancer-associated fibroblasts (CAFs) have different subtypes and play diverse roles in the tumour microenvironment. Here, the authors use single-cell RNA-seq and multiplex imaging mass cytometry data to propose a CAF classification scheme of nine subtypes across different cancer types.

Immune checkpoint therapy in breast cancer remains restricted to triple negative patients, and long-term clinical benefit is rare. The primary aim of immune checkpoint blockade is to prevent or reverse exhausted T cell states, but T cell exhaustion in breast tumors is not well understood. Here, we use single-cell transcriptomics combined with imaging mass cytometry to systematically study immune environments of human breast tumors that either do or do not contain exhausted T cells, with a focus on luminal subtypes. We find that the presence of a PD-1 high exhaustion-like T cell phenotype is associated with an inflammatory immune environment with a characteristic cytotoxic profile, increased myeloid cell activation, evidence for elevated immunomodulatory, chemotactic, and cytokine signaling, and accumulation of natural killer T cells. Tumors harboring exhausted-like T cells show increased expression of MHC-I on tumor cells and of CXCL13 on T cells, as well as altered spatial organization with more immature rather than mature tertiary lymphoid structures. Our data reveal fundamental differences between immune environments with and without exhausted T cells within luminal breast cancer, and show that expression of PD-1 and CXCL13 on T cells, and MHC-I – but not PD-L1 – on tumor cells are strong distinguishing features between these environments. T cell exhaustion in breast tumours remains to be fully characterised. Here, single cell transcriptomics and imaging mass cytometry analysis of luminal breast tumours with or without exhausted T cells suggests distinct patterns of PD-1 and CXCL13 expression in T cells, and of MHC-I, but not PD-L1, expression in tumour cells.

Single-cell transcriptomics is used to determine what cell types are present in brain organoids and how much these cell types vary across organoids.

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. However, detection, inhibition, and elimination of EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. We comprehensively profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a flow cytometry surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by prolonged exposure to TGFβ1 or 4-hydroxytamoxifen, respectively. Each cell line exhibited a spectrum of EMT transition phenotypes, which we compared to the steady-state phenotypes of fifteen luminal, HER2-positive, and basal breast cancer cell lines. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used here, and for the design of EMT time course experiments.Measurement(s)RNA-seq gene expression profiling assay • cell surface proteins • protein expression at the single-cell level • Epithelial-to-Mesenchymal Transition • breast cancer cell • mammary gland epithelial cellTechnology Type(s)mRNA Sequencing • Flow Cytometry • cytometry time of flight assay • Cell CultureFactor Type(s)protein level • gene expression • cell morphologySample Characteristic - OrganismHomo sapiensSample Characteristic - Environmentcell cultureMachine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16989301

Fluorescence nanoscopy is extending its reach into structural biology.

A method using micrococcal nuclease for chromatin fragmentation gives a high-resolution view of 3D genome structure.

A systematic comparison of gene expression patterns in human inflammatory conditions and in their corresponding mouse models raises troubling questions.