Project description:Tenebrio molitor is increasingly used as a sustainable source of nutrients for food and feed production, yet their lipid metabolic responses to dietary protein remain poorly characterized at the molecular species level. Here, we report a targeted UPLC–MS/MS lipidomics dataset of Tenebrio molitor larvae fed four dietary treatments: a wheat bran control and three formulated diets containing 15%, 20% and 25% crude protein. The dataset includes 20 biological samples, with five independent replicates per treatment, each generated from pooled whole-body larvae. Using a scheduled multiple reaction monitoring method on a QTRAP 6500+ platform, 834 lipid species were annotated and quantified across major lipid categories, including glycerolipids, glycerophospholipids, glycolipids and sphingolipids. The data records comprise raw mzML files, processed concentration matrices, sample metadata, lipid annotation tables, MRM transition lists and quality-control metrics. Technical validation using pooled quality-control samples, coefficient-of-variation analysis, sample correlation, principal component analysis and hierarchical clustering supported the reproducibility of the dataset. This resource may facilitate studies of insect nutrition, dietary lipid remodeling, feed formulation and comparative lipidomics.
Project description:We report the single nucleus multiome (RNAseq+ATACseq) of a female mouse pituitary sample. This dataset was generated for supporting the development of a data-driven batch inference method and transforms often heterogeneous data matrices obtained from different samples into a uniformly cell-type annotated and integrated dataset.
Project description:Forensic DNA analysis is well established for phenotyping, providing valuable investigative leads. Recently, proteomics, the large-scale study of proteins, is increasingly recognized as a complementary tool to DNA analysis, particularly for enhancing the evidential value of traces and especially in cases involving degraded samples or challenging matrices. This study aims to extract phenotypic traits directly from whole blood proteomes, using biological sex determination as a starting point. Using LC–MS/MS, proteomes from 100 whole blood samples of known sex were used to train a biological sex classifier. Cross-validation of the model demonstrated the potential of proteomics for accurate sex classification. Key peptides, such as from pregnancy zone protein and ceruloplasmin, were identified as highly important features. To further evaluate the model, mock case samples were generated to simulate real-world case scenarios. However, a large portion of these mock samples were incorrectly classified, which was caused by batch effects. Based on our findings, transitioning from an untargeted assay to a maximally performant analytical targeted assay is the next crucial step needed for implementation into routine forensic application. Overall, this study advocates for the inclusion of proteomics as part of the forensic phenotyping toolkit, while addressing the challenges, opportunities, and recommendations in its implementation.
Project description:This bulk RNAseq dataset is part of a dataset described in the manuscript titled "Fully defined NGN2 neuron protocol reveals diverse signatures of neuronal maturation". This dataset includes NPC derived neurons using a wild type iPSC line, and was used to validate a MS-117 maturation score which attempt to establish a socre system to assess neuronal maturation with iPSCs derived neurons.
Project description:Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs—including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus—encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 µm × 25 µm) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.