Find research datasets worth reusing

The Regulation of Microglia Activity and the Production of IL-1α and IL-6 in the Degenerated Retina by Mesenchymal Stem Cells

Activation of immune response and production of proinflammatory factors plays an important role in the development and progression of retinal degenerative diseases (RDD). For this reason, focusing on immunomodulation is potential option for the efficient ophthalmological therapy. Mesenchymal stem cells (MSCs) have been study in the treatment of RDD mainly due to their regenerative and neuroprotective actions. Nevertheless, MSCs also possess several immunomodulatory properties. Our study shows that NaIO3-induced degeneration increased in vitro and in vivo expression of genes for Interleukin (IL)-1α and IL-6 in the mouse retinal tissue. In addition, intraperitoneal application of NaIO3 increased expression of gene for Iba-1 and infiltration of CD45+CD11b+ cells into the retina. CD45+ population was also responsible for the production of IL-1α while IL-6 was produced by CD45- cells. Cocultivation of degenerated retina in the presence of MSCs decreased the expression and production of both studied cytokines and expression of gene for Iba-1 in the retinal tissue. On contrary, it was observed that MSCs treated with supernatant from degenerated retina increased the expression of genes for cyclooxygenase-2, transforming growth factor-β, programmed-death ligand 1 and nerve growth factor. These results show that MSCs are able to regulate immune reaction in the degenerated retinal environment.

Membrane staining and segmentation of a developing mouse embryo from 4 to 26 cells

<h1>Intent</h1> The role of this dataset is to provide an example of segmentable and trackable data using signal from cell membrane. This dataset contains one file with the pre-processed imaged embryo (imaging.zip -> imaging.tif) and one file with the outcome of a segmentation using Cellpose (segmentation.zip -> segmentation.tif). <blockquote> This embryo corresponds to the embryo "C1" in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a> </blockquote> <h1>Animal</h1> This set of data represents a mouse embryo developing from the 4-cell stage. The embryo at the last processed timepoint has 26 cells. The mother and the father were both mTmG animals (tdTomato anchored at the membrane of the cells). <blockquote> Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a> </blockquote> <h1>Culture conditions</h1> The embryo was imaged in an inverted SPIM from Luxendo (now Bruker), laying at the bottom of a PFE imaging dish. The embryo developed in a small pocket made by deforming the PFE with a glass tip. We used approx. 150μL of KSOM-AA to cutlure the embryos at 37ºC ± 0.2ºC in 5% CO2 and 5% O2. The medium was covered with approx. 100μL of mineral oil. <blockquote> Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a> </blockquote> <h1>Imaging conditions</h1> The tdTomato was excited with a 561nm laser using as little intensity as possible (0% in the settings + a small fraction leaking through the shutter). We used a 561 LP filter to acquire the signal. Imaging was set with a lateral resolution of 0.208μm and a axial resolution of 1μm. Because of technical issues with the stage of the microscope at the time, the resulting axial resolution was ultimately 1.338μm. In total, 181 slices were acquired per time point. Two consecutive timepoints start with 15 minutes of interval. Although this embryo was imaged for a longer period of time, this dataset shows only the first 117 timepoints. <blockquote> Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a> </blockquote> <h1>Processing conditions</h1> <table> <tbody> <tr> <td>Operation</td> <td>Lateral resolution</td> <td>Axial resolution</td> <td>File</td> </tr> <tr> <td>Imaging</td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td>Cropping </td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td>Lateral binning (average)</td> <td>0.416 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td>Isotropic rescaling</td> <td>0.416 μm</td> <td>0.416 μm</td> <td>-</td> </tr> <tr> <td>Lateral binning (average)</td> <td>0.832 μm</td> <td>0.832 μm</td> <td>imaging.tif</td> </tr> <tr> <td>Segmentation (cellpose)</td> <td>0.832 μm</td> <td>0.832 μm</td> <td>segmentation.tif</td> </tr> </tbody> </table>

Flow cytometry data and image data -A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells

Data of figures shown in the main and supporting information of the paper "Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells".

Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity

The raw materials for the draft: "Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity"

opencc-by-4.0Feb 2024View details →

Repository for the Method Article "Oriented artificial nanofibers and laser induced periodic surface structures as substrates for Schwann cells alignment"

Repository containg the underlaying and extended data for the paper "Oriented artificial nanofibers and laser induced periodic surface structures as substrates for Schwann cells alignment".

Dataset: Light-regulated chloroplast morphodynamics in a single-celled dinoflagellate

This repository contains the files for Figures 1-4 of the manuscript and SI figures 2-4. Every folder contains a readme file with further description. Folders contain analyzed and raw microscopy data.

Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma

This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference)     *experimental design  Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.   *extract protocol  PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.   *library construction protocol  Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.   *library strategy  scRNA-seq and scTCR-seq   *data processing step  Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.  Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).    *genome build/assembly  Alignment was performed using prebuilt Cell Ranger human reference GRCh38.   *processed data files format and content  RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format. Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.   <table> <tbody> <tr> <td> file name </td> <td> file checksum </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> da2e006d2b39485fd8cf8701742c6d77 </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> e125fc5031899bba71e1171888d78205 </td> </tr> <tr> <td> PD1CD8_160421_filtered_contig_annotations.csv </td> <td> 927241805d507204fbe9ef7045d0ccf4 </td> </tr> <tr> <td> PD1CD8_190421_filtered_contig_annotations.csv </td> <td> 8ca544d27f06e66592b567d3ab86551e </td> </tr> </tbody> </table>    <table> <tbody> <tr> <td> *processed data file </td> <td> antibodies/tags </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> none </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy </td> </tr> <tr> <td> PD1CD8_160421_filtered_contig_annotations.csv </td> <td> none </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> none </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy </td> </tr> <tr> <td> PD1CD8_190421_filtered_contig_annotations.csv </td> <td> none </td> </tr> </tbody> </table>

Code and data for "Cell shape and orientation control galvanotactic accuracy"

This is the code and the data required to reproduce the results from "Cell shape and orientation control galvanotactic accuracy" Ifunanya Nwogbaga and Brian A. Camley, arXiv:2407.17420 2024, in press in Soft Matter, DOI    https://doi.org/10.1039/D4SM00952E

DiCARN-DNase: Enhancing Cell-to-Cell Hi-C Resolution Using Dilated Cascading ResNet with Self-Attention and DNase-seq Chromatin Accessibility Data

This repository contains the training, validation, and testing data for the DiCARN-DNase project. The GitHub repository is available via https://github.com/OluwadareLab/DiCARN_DNase

Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis

Here we generate an integrative high-resolution map of human atherosclerotic plaques combining single-cell RNA-seq from multiple studies and spatial transcriptomics data from 12 human specimens, with different stages of atherosclerosis. We show cell-type and atherosclerosis-specific expression changes and spatially constrained alterations in cell-cell communication.

opencc-zeroNov 2024View details →

ROSMAP meQTL Results for Bulk tissue cells with CpGs

This dataset contains methylation quantitative trait loci (meQTL) results for the following study: "regionalpcs improve discovery of DNA methylation associations with complex traits" Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*&Dagger;3, Stephen B. Montgomery*&Dagger;1,3 &Dagger; These authors contributed equally as senior authors * Corresponding authors: Tiffany Eulalio (<a href="mailto:eulalio@alumn.stanford.edu">eulalio@alumn.stanford.edu</a>), Daniel Nachun (<a href="mailto:dnachun@stanford.edu">dnachun@stanford.edu</a>), Stephen B. Montgomery (<a href="mailto:smontgom@stanford.edu">smontgom@stanford.edu</a>)  Author affiliations: 1.     Department of Biomedical Data Science, Stanford University, Stanford, CA 2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA 3.     Department of Pathology, Stanford University, Stanford, CA   Dataset description: This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components). Contents: <ul> <li>Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. <code>parquet1</code> includes chromosomes 1-10, and <code>parquet2</code> includes chromosomes 11-22.</li> <li>cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.</li> <li>cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.</li> <li>cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.</li> <li>cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.</li> </ul> This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.

opencc-zeroNov 2024View details →

ROSMAP meQTL Results for Endothelial cells with CpGs

opencc-zeroNov 2024View details →

ROSMAP meQTL Results for Bulk cells with regionalpcs and averages

EBAII n1 scRNAseq : 10X PBMC10K Cell Ranger v3 MEX matrices

<ul> <li>A public dataset created and provided by 10X Genomics, that consists in ~ 10,000 PBMC (peripheral bone marrow cells) from a human donor</li> <li>The experiment was performed with the 3’ capture kit v3</li> <li>The analysis was performed with Cell Ranger v3</li> <li>Mapping was performed on the GRCh38-2020-A manufacturer reference</li> <li>The structure of these Cell Ranger outputs are described <a href="https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-mex-matrices" target="_blank" rel="noopener">here</a></li> <li>The sparse matrix format corresponds to the <a href="https://math.nist.gov/MatrixMarket/formats.html" target="_blank" rel="noopener">Market Exchange Format (MEX)</a></li> <li>For educational purpose (speed), the dataset columns were reduced to 1/5th (from 737,280 to 147,456 barcodes)</li> </ul>

Dataset of "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells"

This dataset supports the article: "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells"   Raw data for the article "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells". For further details see the readme.txt file.

INTERVERTEBRAL DISC NUCLEUS PULPOSUS CELLS CORPUS

Intervertebral disc degeneration (IDD) arises from an intricate imbalance between the anabolic and catabolic processes governing the extracellular matrix (ECM) within the disc. Biochemical processes are complex, redundant and feedback-looped, thus improved integration of knowledge is needed. To address this, a literature-based regulatory network model (RNM) for nucleus pulposus cells (NPC) is proposed, representing the normal state of the intervertebral disc (IVD) cells, in which proteins are represented by nodes that interact among each other through activation and/or inhibition edges. This model includes 33 different proteins and 153 edges by incorporating critical biochemical interactions in IVD regulation, tested in vivo or vitro in human and animal NPC, alongside non-tissue-specific protein-protein interactions. We used the network to calculate the dynamic regulation of each node through a semi-quantitative method. The basal steady state successfully represented the activity of a normal NPC, and the model was assessed through the published literature, by replicating two independent experimental studies in human normal NPC. Pro-catabolic or pro-anabolic shifts of the network activated by nodal perturbations could be predicted. Sensitivity analysis underscored the significant influence of transforming growth factor beta (TGF-β) and interleukin-1 receptor antagonist (IL-1Ra) on the regulation of structural proteins and degrading enzymes within the system. Given the ongoing challenge of elucidating the mechanisms that drive ECM degradation in IDD, this unique IVD RNM holds promise as a tool for exploring and predicting IDD progression, shedding light on IVD phenotypes and guiding experimental research efforts.

Data and Code from: Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients

<div> <div> <div> Abstract Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG- polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in cerebellum of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias. Data and Code Availability Here you will find data and code associated with our manuscript "Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients", Bartelt et al., Sci. Trans. Med. 16, eadn5449 (2024). The data file labeled "HuCb_filtered.rds" is a processed and annotated single-nucleus RNA-seq Seurat object, containing the gene-level count data for the multiplexed snRNA-seq experiment performed on post-mortem human cerebellar tissues from patients with SCA7 and unaffected controls. Data obtained from WT and SCA7-266Q mice as described in our paper can be accessed in the NIH Gene Expression Omnibus under accession number GSE269430. There are three code files numbered 00 through 02 which contain analysis code for snRNA-seq data applied to both the mouse and human datasets. These files are sequential and will take the user from CellRanger output, to filtered and annotated Seurat objects, and include details for subclustering analysis as well as our pseudobulk DEseq2 differential expression approach. There are places where the user may need to modify the code based on their computer system, version of R or Seurat, and whether they are processing the 5 week, 8 week, or human data sets; these locations in the code are marked with comments. <ul> <li>The first file, 00_Preprocessing_MULTIseq, begins with CellRanger filtered_feature_barcode_matrix output, extracts cell barcodes, utilizes the MULTIseq deMULTIplex software to match cell barcodes to oligo barcodes from MULTIseq fastq files, and annotates the Seurat file with metadata. Cell type identification and annotation also takes place in this file. Note: the deMULTIplex step will likely need to be run on a high performance compute cluster.</li> <li>The second file, 01_Seurat_Analysis, uses the filtered and annotated Seurat file to calculate useful QC metrics, investigate disease signals, and perform cell type subclustering analyses.</li> <li>The third file, 02_Pseudobulk_DEseq2, contains custom analysis code to extract raw counts for each cell type and each animal from the Seurat file, and uses the DEseq2 package to calculate DEGs, taking into account biological replicates, and raw read count differences between control and SCA7 animals.</li> </ul> </div> </div> </div>

Rawdata for publication: An Efficient and Stable Polarizing Agent for In-Cell Magic-Angle Spinning Dynamic Nuclear Polarization NMR Spectroscopy

This is the raw dataset for the publication:   An Efficient and Stable Polarizing Agent for In-Cell Magic-Angle Spinning Dynamic Nuclear Polarization NMR Spectroscopy. It contains the EPR and NMR data used in this publication. DOI of this pubcliation: 10.1021/acs.jpclett.4c02709

Human Retina Cell Atlas reference model

This dataset hosts files needed to reproduce the Human Retina Cell Atlas (HRCA) reference model using scArches. The HRCA data can be accessed through several interactive browsers, including <a href="https://data.humancellatlas.org/hca-bio-networks/eye/atlases/retina-v1-0">HCA Data Portal</a>, <a href="https://cellxgene.cziscience.com/collections/4c6eaf5c-6d57-4c76-b1e9-60df8c655f1e">CELLxGENE</a>, <a href="https://retina.cells.ucsc.edu/">UCSC Cell Browser</a>, and <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2805">the Broad Single Cell Portal</a>. Please use these browsers for atlas exploration and visualization. For more information on HRCA, please refer to the HRCA paper (<a href="https://doi.org/10.1101/2023.11.07.566105" rel="nofollow">Li et al.</a>, bioRxiv 2023) and the Github repository at <a href="https://github.com/RCHENLAB/HRCA_reproducibility">https://github.com/RCHENLAB/HRCA_reproducibility</a>. This dataset has been used in the tutorial for the HRCA reference model at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>. Data description: 1. HRCA_snRNA_allcells_rawcounts.h5ad This file contains the cell-by-gene count matrix for over 3.1 million single nuclei and more than 36,000 gene features of the HRCA. Gene features are represented by gene symbols. Please refer to the interactive browsers for atlas exploration, where gene features are mapped to Ensembl IDs. In the cell metadata, "sampleid" indicates sample batches of cells, and "celltype" specifies 123 retina cell types. 2. model.pt This file is the trained reference model using scArches, incorporating 10,000 highly variable features from the full count matrix. It can be directly used for cell type annotation of new retina samples. 3. HRCA_snRNA_allcells_rawcounts_latent.h5ad This file contains the embeddings of all 3.1 million reference single nuclei generated by the trained reference model using scArches. These embeddings can be used to compare with the embeddings of query data for exploration. 4. HRCA_reference_model_gene_id_and_symbol.csv This file contains the mapping of Ensembl IDs to gene symbols for the 10,000 features used in the reference model. This mapping can be used to convert the gene features in a query .h5ad file from gene IDs to gene symbols, allowing cell type labels to be predicted using the trained reference model, which uses gene symbols as gene features. 5. query.h5ad This file contains a cell-by-gene count matrix for a query dataset, designed to support reproducibility in the HRCA reference model tutorial. The "majorclass" column includes pre-annotated major cell classes. Additional details on the tutorial are available at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>. 6. query_latent.h5ad This file contains the embeddings of the query data against the trained reference model. These embeddings can be compared with the reference data embeddings for exploration and visualization.