Project description:The assay for transposase-accessible chromatin using sequencing (ATAC-seq) at single-cell level can provide different perspectives of regulatory patterns based on cell type-specific accessible regions. While human genomic elements have been well studied, understanding how nuclear acid sequence regulates the expression of target genes in a genome-wide level in other organisms remains a major challenge. Batch effects, expensive machines are still constraints nowadays to build a cross-species landscape for further analysis, a platform with high throughput and low cost will be extremely required.Here, we constructed a cross-species accessible chromatin landscape by combinatorial-hybridization-based single-cell ATAC-seq, a single-cell ATAC-seq platform with high throughput and signal-noise ratio using fresh nuclei as input.
Project description:Single-cell mRNA sequencing (scRNA-seq) technologies are reshaping the current cell-type classification system. In previous studies, we built the mouse cell atlas (MCA) and human cell landscape (HCL) to catalog all cell types by collecting scRNA-seq data. However, systematically study for zebrafish (Danio rerio), fruit fly (Drosophila melanogaster) and earthworm (Eisenia andrei) are still lacking. Here, we construct the zebrafish, Drosophila and earthworm cell atlas with Microwell-seq protocols, which provides valuable resources for characterization of diverse cell populations of zebrafish, Drosophila and earthworm, and studying difference between vertebrates and Invertebrates at single cell level.
Project description:Earthworms enhance plant growth but the precise mechanism by which this occurs is not known. An understanding of the mechanism could potentially support changes in agricultural management reducing fertiliser usage and therefore costs and the carbon footprint of agriculture. We conducted a factorial experiment in which 5 strains of wheat were grown in the presence and absence of earthworms under regular watering and droughted conditions. The different wheat strains all responded in a similar fashion. Plant biomass was greater in the presence of earthworms and under regular watering. The presence of earthworms reduced the impact of drought on plant biomass and also slowed down the rate of drying of the droughted soils. Plant nutrient content (N, P, Si) showed no consistent pattern with treatments but total N, P and Si mirrored plant biomass and decreased in the order earthworm-present watered > earthworm-present droughted > earthworm-absent watered > earthworm-absent droughted. Nutrient availability in the soil, as assessed by chemical extractions showed no consistent pattern with treatments. Differential gene expression of plants was greater between watering treatments than between earthworm treatments. Genes that were differentially expressed between the earthworm treatments predominantly related to plant defences, abiotic stress and control of plant growth though a couple were linked to both nitrogen cycling and stress responses. The soil microbiome of the earthworm-present treatments was more associated with nutrient-rich environments, the promotion of plant growth and the suppression of plant pathogens. Our data suggest that enhanced plant growth was due to changes in the microbiome due to earthworm processing of the soil rather than changes in nutrient availability due to the presence of earthworms.
Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas