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226 results for “proteomics data”
Supplementary data for: Effects of thermal acclimation on the proteome of the planarian Crenobia alpina from an alpine freshwater spring
<p>Species' acclimation capacities and their ability to maintain molecular homeostasis outside of ideal temperature ranges will partly predict their success following climate-change induced thermal regime shifts. Theory predicts that ectothermic organisms from thermally stable environments have muted plasticities, and that these species <span>may be</span> particularly vulnerable to temperature increase. Whether such species retained or lost acclimation capacities remains largely unknown. We studied proteome changes in the planarian <em>Crenobia alpina</em>, a prominent member of cold-stable alpine habitats that is considered to be cold-adapted stenotherm. We found that the species' CT<sub>max</sub> is above its experienced habitat temperatures and that different populations exhibit differential CTmax acclimation capacities, whereby an alpine population showed reduced plasticity. In a separate experiment, we acclimated <em>C. alpina</em> individuals from the alpine population to 8, 11, 14, or 17°C over the course of 168 h and compared a comprehensively annotated species-specific proteome. Network analyses of 3399 proteins and protein set enrichment show that while the species' proteome is overall stable across these temperatures, protein sets functioning in oxidative stress response, mitochondria, protein synthesis and turnover are lower abundant following warm acclimation. Proteins associated with an unfolded protein response, ciliogenesis, tissue damage repair, development, and the innate immune system were higher abundant following warm acclimation. Our findings suggest that this species has not suffered DNA decay (e.g., loss of heat-shock proteins) during evolution in a cold-stable environment and retained plasticity in response to elevated temperatures, challenging the notion that stable environments necessarily result in muted plasticity.</p>
Data from: Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean
<p><span>Species identification is pivotal in biodiversity assessments, and proteomic fingerprinting by MALDI-TOF mass spectrometry has already been shown to reliably identify calanoid copepods to species level. However, MALDI-TOF data may contain more information beyond mere species identification. In this study, we investigated different ontogenetic stages (copepodids C1-C6 females) of three co-occurring <em>Calanus</em> species from the Arctic Fram Strait, which cannot be identified to species level based on morphological characters alone. Differentiation of the three species based on mass spectrometry data was without any error. In addition, a clear stage-specific signal was detected in all species, supported by clustering approaches as well as machine learning using Random Forest. More complex mass spectra in later ontogenetic stages as well as relative intensities of certain mass peaks were found as the main drivers of stage distinction in these species. Through a dilution series, we were able to show that this did not result from the higher amount of biomass that was used in tissue processing of the larger stages. Finally, the data were tested in a simulation for application in a real biodiversity assessment by using Random Forest for stage classification of specimens absent from the training data. This resulted in a successful stage-identification rate of almost 90%, making proteomic fingerprinting a promising tool to investigate polewards shifts of Atlantic <em>Calanus</em> species and, in general, to assess stage compositions in biodiversity assessments of Calanoida, which can be notoriously difficult using conventional identification methods.</span></p>
Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
<p><span>The Clarion Clipperton Fracture Zone (CCZ) is a vast deep-sea region harboring a highly diverse benthic fauna, which will be affected by potential future deep-sea mining of metal-rich polymetallic nodules. Despite the need for conservation plans and monitoring strategies in this context, the majority of taxonomic groups remains scientifically undescribed. However, molecular rapid assessment methods such as DNA-barcoding and Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) provide the potential to accelerate specimen identification and biodiversity assessment significantly in the deep-sea areas. In this study, we successfully applied both methods to investigate the diversity of meiobenthic copepods in the eastern CCZ, including the first application of MALDI-TOF MS for the identification of these deep-sea organisms. Comparing several different species delimitation tools for both datasets, we found that biodiversity values were very similar, with Pielou's Evenness varying between 0.97 and 0.99 in all datasets. Still, direct comparisons of species clusters revealed differences between all techniques and methods, which are likely caused by the high number of rare species being represented by only one specimen, despite our extensive dataset of more than 2000 specimens. Hence, we regard our study as a first approach toward setting up a reference library for mass spectrometry data of the CCZ in combination with DNA-barcodes. We conclude that proteome fingerprinting, as well as the more established DNA-barcoding, can be seen as a valuable tool for rapid biodiversity assessments in the future, even when no reference information is available.</span></p>
Data mining antibody sequences for database searching in bottom-up proteomics
<p>Mass spectrometry (MS)-based proteomics is a powerful method for identifying and quantifying antibodies. Among the various MS approaches, bottom-up proteomics is especially effective for analyzing thousands of antibodies in complex mixtures. In this method, proteins are enzymatically digested into smaller peptides, typically using the protease trypsin, which are then analyzed via mass spectrometry. These peptides are matched to sequences in standard databases like UniProt or NCBI-RefSeq for identification.</p> <p>However, a major limitation of this approach is the absence of comprehensive disease-specific antibody databases. Current databases, such as UniProt, include only a fraction of the antibody sequences present in the human body. For instance, as of January 2024, UniProt contains just 38,800 immunoglobulin sequences, far short of the billions of antibodies the human immune system can produce. As a result, relying on such limited databases can lead to under-detection of antibodies, particularly those associated with specific diseases. Expanding antibody databases with disease-specific sequences is crucial for improving the accuracy of MS-based proteomics in identifying antibodies relevant to human health.</p> <p>Recently, through next-generation sequencing of antibody gene repertoires, it has become possible to obtain billions of antibody sequences (in amino acid format) by annotating, translating, and numbering antibody gene sequences. These large numbers of sequences are now available in public databases such as the <a href="https://opig.stats.ox.ac.uk/webapps/oas/" rel="nofollow">Observed Antibody Space</a>. We hypothesize that using these theoretical antibody sequences as new databases for bottom-up proteomics could address the current lack of antibody coverage in standard databases.</p> <p>We developed a workflow to create disease-specific antibody peptide databases for bottom-up proteomics. The workflow details are available on <a href="https://github.com/trinhxt/SDU_Immunoinformatics">GitHub</a>. The database and metadata files generated by this workflow are stored in this Zenodo dataset, and they are used in DAT-DB — a web application that allows researchers to obtain FASTA files of disease-specific antibody peptides for direct use in bottom-up proteomics (see <a href="https://trinhxt.shinyapps.io/DAT-DB/">Demo version</a>).</p> <p>Each database file in this dataset is in <em>.duckdb</em> format and contains tables with 10 columns: Sequence, Filename, Patient, BSource, BType, Isotype, N_patient, N_antibody, Length_aa, and CDR3. The "<strong>Sequence</strong>" column contains tryptic peptides. "<strong>Filename</strong>" is the file where the data was collected. "<strong>Patient</strong>" refers to the patient number as listed in <em>metadata2.csv</em>. "<strong>BSource</strong>" refers to the B-cells' source, and "<strong>BType</strong>" refers to the type of B-cells. "<strong>Isotype</strong>" specifies the antibody isotype (IgA, IgD, IgE, IgG, IgM, or Bulk). "<strong>N_patient</strong>" indicates the number of patients having this peptide, and "<strong>N_antibody</strong>" specifies the number of antibodies containing this peptide. "<strong>Length_aa</strong>" indicates the number of amino acids in the peptide, while "<strong>CDR3</strong>" shows whether the peptide is found in the CDR3 region.</p> <p>The file <em>metadata1.csv</em> contains information about each database file, while <em>metadata2.csv</em> provides details about the sources of the collected antibodies.</p>
Proteome Data for Amyloid Atlas
<p>Datafile for collected and calculated data for proteins in Amyloid Atlas</p>
UTS Proteomics Subject Raw Data
<p>Raw data for the Postgraduate subject 'Proteomics' Subject # 91572. E coli proteins extracted and trypsin digested before Stage Tip clean up with SDB-RPS. Control and Treatment, three replicate injections of each. Run through Thermo QExactive Plus system with Waters M-class chromatograph with self made and packed 35cm column of 1.7um C18 beads.</p>
Cancer-Associated Fibroblast Classification in Single-Cell and Spatial Proteomics Data
<p>ometiff: Imaging Data</p> <p>Cell Masks: Masks generated with cellprofiler from ilastik segmentation training</p> <p>cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments.</p> <p>IMC Data Objects: Single cell experiment RDS files.</p> <p> </p> <p>scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.</p>
Data and analysis of proteomic responses to hexokinase-II depletion in GAL80 and gal80Δ Saccharomyces cerevisiae with an engineered sesquiterpene-pathway
<p>Dataset 1: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx">GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx</a></p> <p>The comparison between strain ILHA o128R+pJT9RFR (dHxk2p) and ILHA o401R+ pJT9RFR (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase and the ethanol growth phase. </p> <p> </p> <p>Dataset 2: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/gal80%CE%94_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx">gal80Δ_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx</a></p> <p>The comparison between strain ILHA NLD128-1 (dHxk2p) and ILHA NLD401 (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase (EXP) and the ethanol growth phase (ETH). </p> <p> </p>
Data sets for "Updated MS²PIP web server supports cutting-edge proteomics applications"
<p>Data sets and code used to train and evaluate new MS²PIP models. </p>
The proteomics raw data from project MIPHA
<p>The proteomics data and search result from the hisD overexpression strains and control strains treated with or without levofloxacin. </p>
Data from: Plasma proteomic signatures of enteric permeability among hospitalized and community children under two years of age in Kenya and Pakistan
<p class="MsoNormal">We aimed to establish if enteric permeability was associated with similar biological processes in children recovering from hospitalization and relatively healthy children in the community. Extreme gradient-boosted models predicting the lactulose rhamnose ratio, a biomarker of enteric permeability, using 7,500 plasma proteins and 34 fecal biomarkers of enteric infection among 89 hospitalized and 60 community children aged 2-23 months were built. The R<sup>2</sup> were calculated in test sets. The models performed better among community (R<sup>2</sup>: 0·27 [min-max: 0·19, 0·53]) than hospitalized children (R<sup>2</sup>: 0·07 [min-max: 0·03, 0·11]). In the community, LRR was associated with biomarkers of humoral antimicrobial and cellular lipopolysaccharide responses, and inversely associated with anti-inflammatory and innate immunological responses. Among hospitalized children, the selected biomarkers had few shared functions.<strong> </strong>This suggests enteric permeability among community children was associated with a host response to pathogens, but this association was not observed among hospitalized children.</p>
Data from: Potential of MALDI−TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages
<p><span>Morphological identification of cnidarian species can be difficult throughout all life stages due to the lack of distinct morphological characters. Moreover, in some cnidarian taxa genetic markers are not fully informative, and in these cases combinations of different markers or additional morphological verifications may be required. Proteomic fingerprinting based on MALDI-TOF mass spectra was previously shown to provide reliable species identification in different metazoans including some cnidarian taxa. For the first time, we tested the method across four cnidarian classes (Staurozoa, Scyphozoa, Anthozoa, Hydrozoa) and included different scyphozoan life-history stages (polyp, ephyra, medusa) into our dataset. Our results revealed reliable species identification based on MALDI-TOF mass spectra across all taxa with species-specific clusters for all 23 analyzed species. In addition, proteomic fingerprinting was successful for distinguishing developmental stages, still by retaining a species specific signal. Furthermore, we identified the impact of different salinities in different regions (North Sea and Baltic Sea) on proteomic fingerprints to be negligible. In conclusion, the effects of environmental factors and developmental stages on proteomic fingerprints seem to be low in cnidarians. This would allow using reference libraries built up entirely of adult or cultured cnidarian specimens for the identification of their juvenile stages or specimens from different geographic regions in future biodiversity assessment studies.</span></p>
Data from: Transcriptome and exosome proteome analyses provide insights into the mantle exosome involved in nacre color formation of pearl oyster Pinctada fucata martensii
<p>The pearl oyster <em>Pinctada fucata martensii</em> is an economically important species of marine pearl culture, and tissue of mantle plays an essential role in pearl formation. Here, the extracted exosomes from mantle of <em>P. f. martensii</em> were analyzed by quantitative protein TMT sequencing. We wanted to verify if exosomes are the important vehicle in the process of biomineralization (e.g., pearl and shell formation), especially the color formation in shellfish. Finally, we got some results which indicated the importance of exosomes in sides of proteins, and it can also contribute to other researches related to the exosomes or pearl oyster.</p>
Data from: Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean
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RNA-seq and label-free quantitative proteomics data from: KDM4A serves as an α-tubulin demethylase regulating microtubule polymerization and cell mitosis
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Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting
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Data from: Linking warmer nest temperatures to reduced body size in seabird nestlings: Possible mitochondrial bioenergetic and proteomic mechanisms
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Data from: Quantitative proteomics reveals rapid divergence in the postmating response of female reproductive tracts among sibling species
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Data from: Pre- and post- treatment fold change plasma proteomics in metastatic NSCLC patients
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Data from: Plasma proteomic signatures of enteric permeability among hospitalized and community children under two years of age in Kenya and Pakistan
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.