Project description:Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.
Project description:This data file contains all the data used to produce Figure 4 of Saldanha, Brauer and Bostein. The limiting nutrient is indicated by the letter followed by the timecourse number. S= sulfate, P=phosphate, U=uracil, L=leucine. The chemostat comparisons are prefixed by CC. Channels are as noted in the individual arrays, and are consistent for a given timecouse, but may vary between time courses. An all pairs experiment design type is where all labeled extracts are compared to every other labeled extract. Keywords: all_pairs
Project description:Previous studies have described the structure and function of the insular cortex in terms of spatially continuous gradients. Here we assess how spatial features of insular resting state functional organization correspond to individual pain sensitivity. From a previous multicenter study, we included 107 healthy participants, who underwent resting state functional MRI scans, T1-weighted scans and quantitative sensory testing on the left forearm. Thermal and mechanical pain thresholds were determined. Connectopic mapping, a technique using non-linear representations of functional organization was employed to describe functional connectivity gradients in both insulae. Partial coefficients of determination were calculated between trend surface model parameters summarizing spatial features of gradients, modal and modality-independent pain sensitivity. The dominant connectopy captured the previously reported posteroanterior shift in connectivity profiles. Spatial features of dominant connectopies in the right insula explained significant amounts of variance in thermal (R2 = 0.076; p < 0.001 and R2 = 0.031; p < 0.029) and composite pain sensitivity (R2 = 0.072; p < 0.002). The left insular gradient was not significantly associated with pain thresholds. Our results highlight the functional relevance of gradient-like insular organization in pain processing. Considering individual variations in insular connectopy might contribute to understanding neural mechanisms behind pain and improve objective brain-based characterization of individual pain sensitivity.
Project description:This data file contains all the data used to produce Figure 4 of Saldanha, Brauer and Bostein. The limiting nutrient is indicated by the letter followed by the timecourse number. S= sulfate, P=phosphate, U=uracil, L=leucine. The chemostat comparisons are prefixed by CC. Channels are as noted in the individual arrays, and are consistent for a given timecouse, but may vary between time courses. An all pairs experiment design type is where all labeled extracts are compared to every other labeled extract. Using regression correlation
Project description:Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.
Project description:Data-independent acquisition (DIA) strategies provide a sensitive and reproducible alternative to data-dependent acquisition (DDA) methods for large-scale quantitative proteomic analyses. Unfortunately, DIA methods suffer from incompatibility with common multiplexed quantification methods, specifically stable isotope labeling approaches such as isobaric tags and stable isotope labeling of amino acids in cell culture (SILAC). Here we expand the use of neutron-encoded (NeuCode) SILAC to DIA applications (NeuCoDIA), producing a strategy that enables multiplexing within DIA scans without further convoluting the already complex MS(2) spectra. We demonstrate duplex NeuCoDIA analysis of both mixed-ratio (1:1 and 10:1) yeast and mouse embryo myogenesis proteomes. Analysis of the mixed-ratio yeast samples revealed the strong accuracy and precision of our NeuCoDIA method, both of which were comparable to our established MS(1)-based quantification approach. NeuCoDIA also uncovered the dynamic protein changes that occur during myogenic differentiation, demonstrating the feasibility of this methodology for biological applications. We consequently establish DIA quantification of NeuCode SILAC as a useful and practical alternative to DDA-based approaches.
Project description:There is a need for targeted analysis of biological fluids for diagnosis, prognosis, or monitoring the progression of diseases. Cerebrospinal fluid (CSF) and serum have been widely used for the development of protein analysis for neurodegenerative diseases and other diseases, respectively. Recently, data-independent acquisition (DIA) mass spectrometry (MS) has been developed to increase the throughput over data-dependent acquisition (DDA) on screening of a large number of samples and discovery of candidate targets. When it comes to target validation, the analytical performance of targeted analysis is critical. However, the inter- and intralaboratory analytical performances of the DIA-MS for targeted proteomic analysis of CSF and serum samples have not yet been investigated. In this study, we showed that the DIA-MS approach allowed us to identify and quantify 1732 CSF and 424 serum proteins, with 90% of proteins identified and quantified in at least 50% of DIA-MS runs. To evaluate the sensitivity, linearity, and dynamic range of the DIA approach, we included the stable isotope-labeled (SI) peptides into CSF and serum samples with serial dilutions. The lower limit of quantification (LLOQ) of peptides was 0.1-0.5 fmol, and the dynamic range was over 3.53 orders of magnitude, with excellent linearity (r2 < 0.978) in CSF and serum samples. Finally, the reproducibility of the DIA-MS approach was evaluated using entire proteins identified in CSF and serum samples. The intralaboratory three replicate results showed reliable reproducibility with 12.5 and 17.3% of the median coefficient of variation (CV) in both CSF and serum matrices, whereas the median CVs of interlaboratory three replicates were 23.8 and 32.0% in CSF and serum samples, respectively. The comparison of the quantitative result between replicates showed close similarity at intra- and interlaboratories with a median Pearson correlation value of >0.98 in CSF and serum, respectively. In conclusion, we demonstrate the capability of the DIA approach as a targeted proteomic analysis for candidate proteins from CSF and serum samples.
Project description:The GLI (GLI1/GLI2) transcription factors have been implicated in the development and progression of prostate cancer although our understanding of how they actually contribute to the biology of these common tumours is limited. We observed that GLI reporter activity was higher in normal (PNT-2) and tumourigenic (DU145 and PC-3) androgen-independent cells compared to androgen-dependent LNCaP prostate cancer cells and, accordingly, GLI mRNA levels were also elevated. Ectopic expression of GLI1 or the constitutively active ΔNGLI2 mutant induced a distinct cobblestone-like morphology in LNCaP cells that, regarding the former, correlated with increased GLI2 as well as expression of the basal/stem-like markers CD44, β1-integrin, ΔNp63 and BMI1, and decreased expression of the luminal marker AR (androgen receptor). LNCaP-GLI1 cells were viable in the presence of the AR inhibitor bicalutamide and gene expression profiling revealed that the transcriptome of LNCaP-GLI1 cells was significantly closer to DU145 and PC-3 cells than to control LNCaP-pBP (empty vector) cells, as well as identifying LCN2/NGAL as a highly induced transcript which is associated with hormone independence in breast and prostate cancer. Functionally, LNCaP-GLI1 cells displayed greater clonal growth and were more invasive than control cells but they did not form colonies in soft agar or prostaspheres in suspension suggesting that they do not possess inherent stem cell properties. Moreover, targeted suppression of GLI1 or GLI2 with siRNA did not reverse the transformed phenotype of LNCaP-GLI1 cells nor did double GLI1/GLI2 knockdowns activate AR expression in DU145 or PC-3 cells. As such, early targeting of the GLI oncoproteins may hinder progression to a hormone independent state but a more detailed understanding of the mechanisms that maintain this phenotype is required to determine if their inhibition will enhance the efficacy of anti-hormonal therapy through the induction of a luminal phenotype and increased dependency upon AR function.
Project description:Effects of rraA deletion on the global transcript profile of E. coli. This experiment set contains primary data for Figure 5B, Lee et. al. Cell 2003. An RNA stablity experiment design type examines stability and/or decay of RNA transcripts. Keywords: RNA_stability_design