Project description:Recent advances in stem cell technology have led to the development of three-dimensional (3D) culture systems called organoids, which have fueled hopes to bring about the next generation of more physiologically relevant high throughput screens (HTS). However, the adaptation of established organoid protocols for HTS applications has so far been elusive. Here, we present a fully scalable, HTS-compatible workflow for the automated generation, maintenance, whole mount staining, clearing, and optical analysis of human neural organoids generated from neural precursor cells in a standard 96-well format. By combining organoid generation and analysis steps in an automated fashion, we can perform quantitative whole-organoid high content imaging with single cell resolution. The resulting organoids are highly homogeneous with regard to their morphology, size, global gene expression, cellular composition, and structure. Calcium imaging suggests organoid-wide synchronized functional coupling. The scalability of our approach has the potential to form the basis for 3D tissue-based screening in a variety of applications including drug development, toxicology studies, and disease modeling.
Project description:Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and Data Independent Acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 of intraand inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.
Project description:The submitted files contain ChIP-seq data for the p300 transcriptional coactivator in GM12878 cells and for the NRSF transcription factor in GM12878 and Jurkat cells generated using a fully automated robotic chromatin immunoprecipitation protocol. Cells were fixed using 1% formaldehyde (NRSF samples) or 1% formaldehyde at 37C (p300 samples).
Project description:Recent advances in sample preparation and analysis have enabled direct profiling of protein expression in single mammalian cells and other trace samples for characterization of cellular heterogeneity and high-resolution mapping of tissues. Several techniques used to prepare and analyze low-input samples employ custom fluidics for nanoliter sample processing and manual sample injection onto a specialized separation column. While effective, these highly specialized systems require significant expertise to fabricate and operate, which has greatly limited the implementation in most proteomics laboratories. Here we developed a fully automated platform termed autoPOTS (Automated Preparation in One pot for Trace Samples) that uses only commercially available instrumentation for sample processing and analysis. An unmodified, low-cost commercial robotic pipetting platform was evaluated and utilized for fully automated sample preparation. We used low-volume 384-well plates and periodically added water or buffer to the microwells to compensate for limited evaporation during sample incubation. Prepared samples were analyzed directly from the well plate with a commercial autosampler that was modified with a 10-port valve for compatibility with 30-µm-i.d. nanoLC columns. We used autoPOTS to analyze 1–500 HeLa cells and observed only a modest reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to our previously developed nanoPOTS platform. As a test of clinical feasibility, we used autoPOTS to identify an average of 1070 protein groups from ~130 fluorescence-activated cell sorted (FACS) B or T lymphocytes. The dataset here includes all the raw files of HeLa cells and lymphocytes. We anticipate that the simplicity and ease of implementation of autoPOTS will make it an attractive option for low-input and single-cell proteomics in many laboratories.
Project description:Stem-cell-derived epithelial organoids are routinely used for the biological and biomedical modelling of tissues. However, the complexity, lack of standardization and quality control of stem cell culture in solid extracellular matrices hampers the routine use of the organoids at industrial scale. Here, we report the fabrication of microengineered cell-culture devices and scalable and automated methods for the suspension culture and real-time analysis of thousands of individual gastrointestinal organoids trapped in microcavity arrays within a polymer-hydrogel substrate. The absence of a solid matrix significantly reduces organoid heterogeneity, as we show for mouse and human gastrointestinal organoids. We used the devices to screen for anticancer drug candidates with patient-derived colorectal cancer organoids, and high-content image-based phenotypic analyses to reveal insights into drug-action mechanisms. The scalable organoid-culture technology should facilitate the use of organoids in drug development and diagnostics.
Project description:Although tandem mass tag (TMT)-based isobaric labeling has become a powerful technique for multiplexed protein quantitation, it has not been easy to automate the workflow for widespread adoption. This is because preparation of TMT labeled peptide samples involves multiple steps ranging from protein extraction, denaturation, reduction and alkylation to tryptic digestion, desalting, labeling with TMT reagents and cleanup, all of which require a high level of proficiency. The variability resulting from multiple processing steps is inherently problematic especially with large-scale studies such as clinical studies that involve hundreds of samples where reproducibility is critical for quantitation. Here, we sought to compare the performance of a recently introduced platform, AccelerOme, for automated proteomics workflows for TMT-labeling experiments with manual processing of samples. Cell pellets were prepared and subjected to a 16-plex experiment using the automated platform and a conventional manual protocol. Single shot LC-MS/MS analysis revealed a higher number of proteins and peptides identified using the automated platform. Efficiencies of tryptic digestion, alkylation and TMT labeling were similar both in manual and automated process. In addition, comparison of quantitation accuracy and precision showed similar performance in automated workflow compared to manual sample preparation. Overall, we demonstrated that the automated platform performs at a level similar to manual process in TMT-based proteomics. We expect that the automated workflow will increasingly replace manual work and be applied to large-scale TMT-baed studies providing robust results and high sample throughput.
Project description:Extracellular vesicles (EVs) have emerged as a promising source of disease biomarkers for non-invasive early-stage diagnoses, but a bottleneck in EV sample processing restricts their immense potential in clinical applications. Existing methods are limited by low EV yield and integrity, slow processing speeds, low sample capacity, and poor recovery efficiency. We aimed to address these issues with a high-throughput, automated workflow for EV isolation, EV lysis, protein extraction, and protein denaturation. The automation can process clinical urine samples in parallel, resulting in protein-covered beads ready for various analytical methods, including immunoassays, protein quantitation assays, and mass spectrometry. Compared to the standard manual lysis method for contamination levels, efficiency, and consistency of EV isolation, the automated protocol shows reproducible and robust proteomic quantitation with less than 10% median coefficient of variation. When we applied the method to clinical samples, we identified a total 3,793 unique proteins and 40,380 unique peptides, with 992 significantly upregulated proteins in kidney cancer patients versus healthy controls. These upregulated proteins were found to be involved in several important kidney cancer metabolic pathways also identified with a manual control. This hands-free workflow represents a practical EV extraction and profiling approach that can benefit both clinical and research applications, streamlining biomarker discovery, tumor monitoring, and early cancer diagnoses.
Project description:Intestinal organoids have widespread applications across a variety of fields, but the dependency on basement membrane extract (BME) has hampered the transition towards clinical utilization. To overcome the limitations of BMEs, we identified an easy-to-use and fully defined extracellular matrix for the culture of human colon organoids established directly from tissue biopsies. Human colonic organoids were established from crypt-derived single cells (from six different patient samples) and cultured in QGel® CN99 or the BME benchmark Matrigel® for six culture passages (median 58 days), after which RNA was harvested for further analysis. The CN99 matrix effectively enabled organoid formation and growth, as well as efficient cell expansion, completely by-passing the use of BME. We expect this fully defined matrix to improve the reproducibility of organoid studies and to advance the translational use of the organoid technology.