Biology subjects

Sabatier, P.

Publications and source records attributed to Sabatier, P..

10 recordsLinked to original sources

bioRxiv2025

The Human Cell Line Phosphoproteome Atlas: A Deep Empirical Resource Revealing Kinase Activity Landscapes

Protein phosphorylation orchestrates cellular signaling and controls diverse biological processes, with its dysregulation driving diseases, notably cancer. Comprehensive, high-throughput phosphoproteomics remains limited by detection sensitivity, data completeness, and computational bottlenecks, especially in low-input settings. We present the deepest empirical human phosphoproteome resource to date, regrouping over 200,000 class I phosphosites across 33 diverse human cell lines, and demonstrate that this spectral library dramatically improves single-shot phosphoproteomics with 30-fold faster data processing compared to library-free approaches and enhanced confidence in phosphosite localization even from minimal sample input. Integrating proteome and phosphoproteome data, we developed a combined kinase activity score, revealing cell line- and cancer-specific signaling vulnerabilities, many correlating with drug sensitivity. This resource accelerates deep, reproducible phosphoproteomics, enabling systematic functional mapping of cellular signaling networks, and empowers precision oncology by highlighting actionable kinase targets in diverse cell states.

biochemistry↗

bioRxiv2025

Single-cell proteomics of pancreatic islet cells reveals type 1 diabetes and donor-specific features

Type 1 diabetes mellitus (T1DM) is the most common severe chronic disease in children and adolescents and requires life-long exogenous insulin treatment. Investigating key differences between the pancreas of healthy and T1DM patients is key to help uncover new treatments. Here we performed a single-cell proteomic (SCP) analysis of purified pancreatic islets from several healthy and T1DM donors at a depth of > 4000 protein quantified. We employed tools designed for single-cell mRNA sequencing (scRNAseq) for cell type annotation highlighting discrepancies between mRNA and protein markers. Subsequently, we defined a subset of markers useful for future proteomics analyses of pancreatic islets. This marker list was employed to refine the annotations of our combined dataset, which we exploited with a machine learning approach to successfully transfer annotations into a new islet dataset. Lastly, we showcase the applicability and value of SCP in a clinical context by providing novel insights into cell type-specific differences between healthy and T1DM islets as well as between healthy islets of different donors. Our study is the first SCP analysis of pancreatic islets from multiple donors and T1DM islets, performed at this level of analytical depth. This provides methodological and biological insight as well as a reference dataset for future SCP studies and for any researcher interested in T1DM and in pancreatic islet physiology.

biochemistry↗

bioRxiv2024

Global analysis of protein turnover dynamics in single cells

Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from [~]4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional measurements beyond protein abundance. This method distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner.

systems biology↗

bioRxiv2023

High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell

The emergence of mass spectrometry (MS)-based single-cell proteomics (SCP) promise to revolutionize the study of cellular biology and biomedicine by providing an unparalleled view of the proteome in individual cells. Despite its groundbreaking potential, SCP is nascent and faces challenges including limited sequence depth, throughput, and reproducibility, which have constrained its broader utility. This study introduces key methodological advances, which considerably improve the sensitivity, coverage and dependability of protein identification from single cells. We developed an almost lossless SCP workflow encompassing sample preparation to MS analysis, doubling the number of identified proteins from roughly 2000 to over 5000 in individual HeLa cells. A comprehensive evaluation of analytical software tools, alongside strict false discovery rate (FDR) controls solidified the reliability of our results. These enhancements also facilitated the direct detection of post-translational modifications (PTMs) in single cells, negating the need for enrichment and thereby simplifying the analytical process. Although throughput in MS remains a challenge, our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. Moreover, an optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining the overall proteomic analysis. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications ranging from the study of cellular development to disease progression and the identification of cell type-specific markers and therapeutic targets.

systems biology↗

bioRxiv2023

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes

We present One-Tip, a lossless proteomics methodology that seamlessly combines swift, one-pot sample preparation with narrow-window data-independent acquisition mass spectrometric analysis. With simplest sample processing, One-Tip reproducibly identifies > 9,000 proteins from [~]1000 cells and [~] 6,000 proteins in a single mouse zygote with a throughput of 40 samples-per-day. This easy-to-use method expands capabilities of proteomics research by enabling greater depth, scalability and throughput covering low to high input samples.

biochemistry↗

bioRxiv2022

System-wide profiling by proteome integral solubility alteration assay of drug residence times for target characterization

Most drugs used in the clinic and drug candidates target multiple proteins, and thus detailed characterization of their efficacy targets is required. While current methods rely on quantitative measurements at thermodynamic equilibrium, kinetic parameters such as the residence time of a drug on its target provide a better proxy for efficacy in vivo. Here, we present Residence Time Proteome Integral Solubility Alteration (ResT-PISA) assay which provides monitoring temporal protein solubility profiles after drug removal ("off-curve") in cell lysate or intact cells, quantifying the lifetime of drug-target interaction. A compressed version of the assay measures the integral under the off-curve enabling the multiplexing of binding affinity and residence time assessments into a single proteomic analysis. We introduce a combined scoring system for three parametric dimensions to improve prioritization of targets. By providing complementary information to other characteristics of drug-target interaction, ResT-PISA approach will be useful in drug development and precision medicine.

biochemistry↗

bioRxiv2021

A spindle and thread-mechanism unblocks translation of N-terminally disordered proteins

Protein disorder is a major hurdle for structural biology. A prominent example is the tumour suppressor p53, whose low expression levels and poor conformational stability due to a high degree of disorder pose major challenges to the development of cancer therapeutics. Here, we address these issues by fusing p53 to an engineered spider silk domain termed NT*. The chimeric protein displays highly efficient translation in vitro and in E. coli and is fully active in human cancer cells. The transmission electron microscopy structure and native mass spectrometry reveal that the full-length p53 fusion protein adopts a compact conformation. Molecular dynamics simulations show that the disordered transactivation domain of p53 is wound around the NT* domain via a series of folding events, resulting in a globular structure. We find that expression of B-Raf, another partially disordered cancer target, is similarly enhanced by fusion to NT*. In summary, we demonstrate how inducing co-translational folding via a molecular "spindle and thread" mechanism can overcome poor translation efficiency of partially disordered proteins.

biochemistry↗

bioRxiv2018

Proteome Integral Stability Alteration assay dramatically increases throughput and sensitivity in profiling factor-induced proteome changes

Various factors, including drugs as well as non-molecular influences, induce alterations in the stability of proteins in cell lysates, living cells and organisms. These alterations can be probed by applying a stability-modifying agent, such as elevated temperature, to a varying degree. As a second dimension of variation, drug concentration or factor intensity can be used. However, the corresponding analysis scheme has a low throughput and high cost. Additionally, since traditional data analysis employs curve fitting, proteins with unusual behavior are frequently ignored. The novel Proteome Integral Stability Alteration (PISA) assay avoids these issues altogether, increasing the analysis throughput by one to two orders of magnitude for unlimited number of parameter variation points. The consumption of the compound and biological material decreases by the same factor. We envision widespread use of the PISA approach in chemical biology and drug development.

cancer biology↗

bioRxiv2018

System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA)

Despite the immense importance of enzyme-substrate reactions, there is a lack of generic and unbiased tools for identifying and prioritizing substrate proteins which are modulated in the structural and functional levels through modification. Here we describe a high-throughput unbiased proteomic method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that enzymatic post-translational modification of substrate proteins might change their thermal stability. SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems in up to a depth of 7179 proteins. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, open new opportunities in investigating the effect of PTMs on signal transduction, and facilitate drug discovery.

biochemistry↗

bioRxiv2018

ProTargetMiner: A proteome signature library of anticancer molecules for functional discovery

We present a publicly available, expandable proteome signature library of anticancer molecules in A549 adenocarcinoma cells. Based on 287 proteomes affected by 56 drugs, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. By employing the specificity concept in partial least square modeling, deconvolution of drug targets and mechanistic proteins is achieved for most compounds, including some kinase inhibitors. We built the first protein co-regulation database that takes into account both protein expression and degradation. A surprising number of strong anti-correlations is found, underscoring the importance of protein repression in cell regulation. Our analysis uncovered a group of proteins with extremely steady expression which are likely essential for core cellular functions. These findings bring about deeper understanding of cell mechanics. Extension of the dataset to novel compounds will facilitate drug design. The introduced specificity concept and modeling scheme are beneficial in other analysis types as well.\n\nStatement of SignificanceProTargetMiner is the first of its kind library of proteome responses of human cancer cells to anticancer molecules. This expandable resource facilitates the deconvolution of drug targets, action mechanisms, and cellular effects. It reveals death modalities, uncovers protein co-regulation and anti-correlation networks and defines the \"untouchable\" proteome essential for core cellular functionalities.

cell biology↗