Single Cell Proteomics Analysis of Drug Response Shows its Potential as Drug Discovery Platform
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap HFX
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Bogdan Budnik
PROVIDER: MSV000088243 | MassIVE | Sun Oct 17 16:39:00 BST 2021
REPOSITORIES: MassIVE
Similar Datasets
Project description:Recent developments in genomic sequencing technology have enabled comprehensive transcriptome analyses of single cells. In contrast, single cell proteomics analyses have been restricted to targeted analyses, for example using flow cytometry with GFP fusions or mass cytometry. Here, we performed global absolute protein quantification of single Xenopus laevis eggs using mass spectrometry-based proteomics. We quantified over 5800 proteins, thus representing the largest single cell proteome that has been characterized to date. Absolute protein amounts in single eggs are highly comparable, thus indicating a tight regulation of global protein abundance. Comparison between the single-cell proteome and transcriptome reveal poor expression correlation. Finally, we identified 439 proteins that significantly change in abundance during early embryogenesis. Many of these proteins do not show regulation at the transcript level. Altogether, our data reveal that the transcriptome is a poor indicator of the proteome and that protein levels are tightly controlled in Xenopus leavis eggs. RNA-seq in Xenopus laevis of 5 replicates of both single eggs and single embryos.
2014-08-04 | E-GEOD-56586 | biostudies-arrayexpress
Project description:Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture and isobaric Tandem Mass Tag labeling enabled up to 32 single cells to be analyzed in a single LC-MS analysis, in addition to reference and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ~280 single cells were analyzed per day, which could be increased to ~700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions. Despite some decrease in coverage, this method readily differentiated four different cell types and thus proves suitable for, e.g., rapid cell typing.
2023-07-11 | PXD040455 | Pride
Project description:Recent developments in genomic sequencing technology have enabled comprehensive transcriptome analyses of single cells. In contrast, single cell proteomics analyses have been restricted to targeted analyses, for example using flow cytometry with GFP fusions or mass cytometry. Here, we performed global absolute protein quantification of single Xenopus laevis eggs using mass spectrometry-based proteomics. We quantified over 5800 proteins, thus representing the largest single cell proteome that has been characterized to date. Absolute protein amounts in single eggs are highly comparable, thus indicating a tight regulation of global protein abundance. Comparison between the single-cell proteome and transcriptome reveal poor expression correlation. Finally, we identified 439 proteins that significantly change in abundance during early embryogenesis. Many of these proteins do not show regulation at the transcript level. Altogether, our data reveal that the transcriptome is a poor indicator of the proteome and that protein levels are tightly controlled in Xenopus leavis eggs.
2014-08-04 | GSE56586 | GEO
Project description:Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.
2022-01-28 | PXD027912 | Pride
Project description:Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment specific drug responses in thousands of single-cell datasets, we developed Trellis — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy.
2023-12-06 | GSE239386 | GEO
Project description:The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional (2D) monolayers on plastic. However, many cellular features are impaired in these unnatural conditions and big alterations in gene expression in comparison to tumors have been reported. Three-dimensional (3D) cell culture models have become increasingly popular and are suggested to be better models than 2D monolayers due to improved cell-to-cell contacts and structures that resemble in vivo architecture. The aim of this study was to develop a simple high-throughput 3D drug screening method and to compare drug responses in JIMT1 breast cancer cells when grown in 2D, in polyHEMA coated anchorage independent 3D models and in Matrigel on-top 3D cell culture models. We screened 102 compounds with multiple concentrations and biological replicates for their effects on cell proliferation. The cells were either treated immediately upon plating or they were allowed to grow in 3D for four days prior to the drug treatment. Big variations in drug responses were observed between the models indicating that comparisons of culture model influenced drug sensitivities cannot be made based on effects of a single drug. However, we show with the 63 most prominent drugs that, in general, JIMT1 cells grown on Matrigel were significantly more sensitive to drugs than cells grown in 2D cultures, while responses of cells grown in polyHEMA resembled those of 2D. Furthermore, comparison of gene expression profiles of the cell culture models to xenograft tumors indicated that cells cultured in Matrigel and as xenografts most closely resembled each other. In this study we also suggest that 3D cultures can provide a platform for systematic experimentation of larger compound collections in a high-throughput mode and be used as alternatives for traditional 2D screens towards better comparability to in vivo state. Gene expression analysis of JIMT1 breast cancer cells cultured as xenografts for 43 days, in two dimensional cultures for seven days (2D7d), in polyHEMA three dimensional cell culture models for four and seven days (PH7d and PH7d), and in Matrigel three dimensional cultures for four and seven days (MG4d and MG7d). Two biological replicates was included for each sample.
2013-10-30 | E-GEOD-42529 | biostudies-arrayexpress
Project description:Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations ($97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.
2022-06-25 | GSE206741 | GEO
Project description:Heterogeneity between patients poses a significant challenge in cancer drug development. Although success of a drug candidate in the clinic depends on efficacy across large numbers of diverse patients, only a few in vivo preclinical models are used for assaying a preclinical drug candidate due to the lack of scalability of traditional xenograft models. To address this limitation, we developed GENEVA, a scalable platform that enables the measurement of molecular and phenotypic responses to drug perturbations at single-cell resolution, both in 3D and in vivo. GENEVA models the genetic diversity of cancer by combining multiple patient-derived cell lines and cancer cell lines into pooled 3D cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. This platform integrates high-resolution transcriptomics with multiplexed single-cell profiling, providing a unique ability to uncover the interplay between cancer genotypes and drug responses. In this study, we apply GENEVA to investigate KRAS G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition. Additionally, we identify epithelial to mesenchymal transition (EMT) as a prominent resistance mechanism to KRAS G12C inhibition using in vivo GENEVA mice. These findings highlight the utility of GENEVA in identifying novel therapeutic targets and optimizing combination therapies, while underscoring its potential to bridge the gap between preclinical cancer models and patient outcomes by modeling the complexity of tumor heterogeneity in a scalable and robust assay.
2024-12-26 | GSE283141 | GEO
Project description:Heterogeneity between patients poses a significant challenge in cancer drug development. Although success of a drug candidate in the clinic depends on efficacy across large numbers of diverse patients, only a few in vivo preclinical models are used for assaying a preclinical drug candidate due to the lack of scalability of traditional xenograft models. To address this limitation, we developed GENEVA, a scalable platform that enables the measurement of molecular and phenotypic responses to drug perturbations at single-cell resolution, both in 3D and in vivo. GENEVA models the genetic diversity of cancer by combining multiple patient-derived cell lines and cancer cell lines into pooled 3D cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. This platform integrates high-resolution transcriptomics with multiplexed single-cell profiling, providing a unique ability to uncover the interplay between cancer genotypes and drug responses. In this study, we apply GENEVA to investigate KRAS G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition. Additionally, we identify epithelial to mesenchymal transition (EMT) as a prominent resistance mechanism to KRAS G12C inhibition using in vivo GENEVA mice. These findings highlight the utility of GENEVA in identifying novel therapeutic targets and optimizing combination therapies, while underscoring its potential to bridge the gap between preclinical cancer models and patient outcomes by modeling the complexity of tumor heterogeneity in a scalable and robust assay.
2024-12-26 | GSE282350 | GEO