A Seurat object (.rds format) for a single-cell ATAC-seq dataset of hematopoietic stem and progenitor cells. It includes 4 samples:controlDKO (Reg1–/–, Reg3–/–)Nfkbiz–/–TKO DKO (Reg1–/–, Reg3–/– Nfkbiz–/–)Data was processed using Seurat and Signac. For more details we refer to the accompanying GitHub repository. In brief, we normalized the data, conducted linear and non-linear dimensionality reduction, clustered cells, calculated "gene activities", and added motif information to the Seurat object.A link to the accompanying paper will be added here after publication.

RDS files containing processed Seurat objects for multiome analysis of neuroblastoma cell lines. File names reflect the cell line.

This repository contains data to be used in the Single Cell RNA + ATAC integration and data analysis IRTG course 2024.

The preprocessed Seurat object and the two Scanpy objects that can be used to run the scANANSE pipeline with.

Seurat object: preprocessed_PBMC.Rds

Scanpy objects: rna_PBMC.h5ad, atac_PBMC.h5ad

Additional raw data, used to construct the preprocessed objects, supplemented from:

https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz.tbi,
https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat

This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. Please refer to the READMEs in the directories, which are summarized below.

The record contains the following files:

`clusters.tsv`: contains the cluster id, name and colour of clusters in the paper

scATAC.zip

Analysis products for the single-cell ATAC-seq data. Contains:

- `cells.tsv`: list of barcodes that pass QC. Columns include:
- `barcode`
- `sample`: (time point)
- `umap1`
- `umap2`
- `cluster`
- `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root
- `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root
- `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.
- `features.tsv`: 50 dimensional representation of each cell
- `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`

scATAC_clusters.zip

Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data.

- `clusters.tsv`: contains the cluster id, name and colour used in the paper
- `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.
- `fragments`: contains per cluster fragment files

scATAC_scRNA_integration.zip

Analysis products from the integration of scATAC with scRNA. Contains:

- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.
- `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.
- `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.

scRNA.zip

Analysis products for the single-cell RNA-seq data. Contains:

- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)
- `genes.txt`: list of all genes
- `cells.tsv`: list of barcodes that pass QC across samples. Contains:
- `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC)
- `sample`: sample name (D0, D2, .., D14, iPSC)
- `umap1`
- `umap2`
- `nCount_RNA`
- `nFeature_RNA`
- `cluster`
- `percent.mt`: percent of mitochondrial transcripts in cell
- `percent.oskm`: percent of OSKM transcripts in cell
- `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`
- `pca.tsv`: first 50 PC of each cell
- `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`

multiome.zip

multiome/snATAC:

These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2).

- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:
- `barcode`
- `umap1`: These are the coordinates used for the figures involving multiome in the paper.
- `umap2`: ^^^
- `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq
- `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels.
- `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.
- `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.
- `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.
- `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.

multiome/snRNA:

- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.
- `genes.txt`: list of all genes (this is different from the list in scRNA analysis)
- `cells.tsv`: list of barcodes that pass QC across samples. Contains:
- `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively)
- `sample`: sample name (D1M, D2M)
- `nCount_RNA`
- `nFeature_RNA`
- `percent.oskm`: percent of OSKM genes in cell
- `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).

The small freshwater cnidarian polyp Hydra vulgaris uses adult stem cells (interstitial stem cells) to continually replace neurons throughout its life. This feature, combined with the ability to image the entire nervous system (Badhiwala et al., 2021; Dupre & Yuste, 2017) and the availability of gene knockdown techniques (Juliano, Reich, et al., 2014; Lohmann et al., 1999; Vogg et al., 2022), makes Hydra a tractable model for studying nervous system development and regeneration at the whole-organism level. In this study, we use single-cell RNA sequencing and trajectory inference to provide a comprehensive molecular description of the adult nervous system. This includes the most detailed transcriptional characterization of the adult Hydra nervous system to date. We identified eleven unique neuron subtypes together with the transcriptional changes that occur as the interstitial stem cells differentiate into each subtype. Towards the goal of building gene regulatory networks to describe Hydra neuron differentiation, we identified 48 transcription factors expressed specifically in the Hydra nervous system, including many that are conserved regulators of neurogenesis in bilaterians. We also performed ATAC-seq on sorted neurons to uncover previously unidentified putative regulatory regions near neuron-specific genes. Finally, we provide evidence to support the existence of transdifferentiation between mature neuron subtypes and we identify previously unknown transition states in these pathways. Altogether, we provide a comprehensive transcriptional description of an entire adult nervous system, including differentiation and transdifferentiation pathways, which provides a significant advance toward understanding mechanisms that underlie nervous system regeneration.

Cell lines and compounds PCa cell lines (LNCaP, 22Rv1, VCaP, PC3, DU145, NCI-H660, C4-2), other cell lines (HEK293T, DLD1) and benign prostate line (RWPE-1) were purchased from ATCC and maintained according to ATCC protocols. Patient-derived CRPC organoids (WCM and MSK) were established and maintained as organoids in Matrigel drops according to the previously described protocol70. LNCaP-AR cells were a kind gift from Dr. Sawyers and Dr. Mu (Memorial Sloan Kettering Cancer Center) and were cultured as previously described5. All used cell lines and their phenotype are listed in Supplementary Table 1. Cell cultures were regularly tested for Mycoplasma contamination and confirmed to be negative. Genentech Inc. synthesized A947, its epimer (A858), FHD-286 and AU-15330. Cobimetinib, Trametinib, VL285 and CHIR99021 were purchased from SelleckChem. BRM014 was purchased from MedChemExpress. All drugs used in this study are listed in Supplementary Table 2. Single-cell RNA-sequencing by SORT-seq library generation and analysis SORT-seq was performed using Single Cell Discoveries (SCD) service. Organoids were treated for 72h with a control epimer (A858) or active compound (A947) at 1 µM, and 1x10e6 cells were harvested in PBS. Harvested cells were stained with 100ng/ml DAPI to stain dead cells. Using a cell sorter (conducted by Flow Cytometry Core, DBMR, Bern) and the recommended settings (Single Cell Discoveries B.V.), DAPI-negative cells were sorted as single cells in 376 wells of four 384-well plates containing immersion oil per condition. Resulting in a theoretical cell number of 1504 cells per condition. All post-harvesting steps were performed at 4°C. Plates were snap-frozen on dry ice for 15 minutes and sent out for sequencing at Single Cell Discoveries B.V. Data were analyzed using the Seurat package v.4.3.080. Cell QC filtering was done using the following thresholds: nCount > 4000, nFeature > 1000, percent.mito 0.85. Differential gene expression analysis between clusters was done with Seurat::FindAllMarkers. Module scores were generated with Seurat::AddModuleScore. Gene set enrichment analysis was done with the package fgsea v.1.24.081 and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Gene regulatory networks analysis was done with pySCENIC v.0.12.182. Overall analysis was done in R v.4.2.2. RNA-seq library generation and processing For bulk RNA-seq, organoids were treated with A858 or A947 (1µM) for 24h and 48h (3 biological replicates per condition). RNA was extracted using the RNeasy Kit (Qiagen); library generation and subsequent sequencing was performed by the clinical genomics lab (CGL) at the University of Bern. Sequencing reads were aligned against the human genome hg38 with STAR v.2.7.3a83. Gene counts were generated with RSEM v.1.3.284, whose index was generated using the GENCODE v33 primary assembly annotation. Differential gene expression analysis was done with DESeq2 v.1.34.085. Gene set enrichment analysis was done with the package fgsea v.1.20.081 and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Analysis was done in R v.4.1.2. TCF7L2 ChIP-seq library generation and processing For the ChIP-Seq assay, chromatin was prepared from 2 biological replicates of WCM1078 treated with A858 or A947 (1µM) for 4h, and ChIP-Seq assays were then performed by Active Motif Inc. using an antibody against TCF7L2 (Santa Cruz, cat# sc-8631, Lot# D0914). ChIP-seq sequence data was processed using an ENCODE-DC/chip-seq-pipeline2 -based workflow (https://github.com/ENCODE-DCC/chip-seq-pipeline2). Briefly, fastq files were aligned on the hg38 human genome reference using Bowtie2 (v2.2.6) followed by alignment sorting (samtools v1.7) of resulting bam files with filtering out of unmapped reads and keeping reads with mapping quality higher than 30. Duplicates were removed with Picard’s MarkDuplicates (v1.126) function, followed by indexation of resulting bam files with samtools. For each bam file, genome coverage was computed with bedtools (v2.26.0), followed by the generation of bigwig (wigToBigWig v377) files. Peaks were called with macs2 (v2.2.4) for each treatment sample using a pooled input alignment (.bam file) as control. Downstream analyses were performed with DiffBind v3.11.1 with default parameters, except for summits=250 in dba.count(). dba.contrast() and dba.analyzed() were used to compute significant differential peaks with DESeq2. ATAC-seq library generation and processing ATAC-seq was performed from 50’000 cryo-preserved cells per condition (1µM A858 and 1µM A947, n = 3 biological replicates) treated for 4h and analyzed as described in previous study86. Briefly, 50,000 cryo-preserved cells per condition were lysed for 5 minutes on ice and tagmented for 30 minutes at 37°C, followed by DNA isolation. DNA was barcoded and amplified before sequencing. PRO-cap library generation and processing For PRO-cap, app...

Datasets to go along with the publication listed:

full_object.rds: Brain Organoid Phospho-Seq dataset with ATAC, Protein and imputed RNA data

rna_object.rds: Reference whole cell scRNA-Seq object on Brain organoids

multiome_object.rds: Bridge dataset containing RNA and ATAC modalities for Brain organoids

metacell_allnorm.rds: Metacell object for finding gene-peak-protein linkages in Brain organoid dataset

fullobject_fragments.tsv.gz: fragment file to go with the full object

fullobject_fragments.tsv.gz.tbi:index file for the full object fragment file

multiome_fragments.tsv.gz: fragment file to go with the multiome object

multiome_fragments.tsv.gz.tbi:index file for the multiome object fragment file

K562_Stem.rds : object corresponding to the pilot experiment including K562 cells and iPS cells

K562_stem_fragments.tsv.gz: fragment file to go with the K562_stem object

K562_stem_fragments.tsv.gz.tbi: index file for the K562_stem object fragment file

retina.rds : object corresponding to the retinal organoid phospho-seq experiment

retina_fragments.tsv.gz: fragment file to go with the retina object

retina_fragments.tsv.gz.tbi: index file for the retina object fragment file

retina_multi.rds : object corresponding to the retinal organoid phospho-seq-multiome experiment

retina_multi_fragments.tsv.gz: fragment file to go with the retina_multi object

retina_multi_fragments.tsv.gz.tbi: index file for the retina_multi object fragment file

To use the K562, multiome, retina and retina_multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:

obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))

To use the K562 and multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:

obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))

To use the "fullobject" dataset provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:

#load the stringr package library(stringr) #load the object obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL #Remove unwanted residual information and rename cells obj@reductions$norm.adt.pca <- NULL obj@reductions$norm.pca <- NULL obj <- RenameCells(obj, new.names = str_remove(Cells(obj), "atac_")) # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))