Datasets and Code accompanying the new release of RCA, RCA2. The R-package for RCA2 is available at GitHub: https://github.com/prabhakarlab/RCAv2/

The datasets included here are:

Datasets required for a characterization of batch effects:

merged_rna_seurat.rds

de_list.rds

mergedRCAObj.rds

merged_rna_integrated.rds

10X_PBMCs.RDS: Processed 10X PBMC data RCA2 object (10X PBMC example data sets )

NBM_RDS_Files.zip: Several RDS files containing RCA2 object of Normal Bone Marrow (NBM) data, umap coordinates, doublet finder results and metadata information (Normal Bone Marrow use case)

Dataset used for the Covid19 example:

blish_covid.seu.rds

rownames_of_glocal_projection_immune_cells.txt

Blish_RCA_no_QC_filtering_project_to_multiple_panels.rds

Data sets used to outline the ability of supervised clustering to detect disease states:

809653.seurat.rds

blish_covid.seu.rds

Performance benchmarking results:

Memory_consumption.txt

rca_time_list.rds

ScanPY input files:

input_data.zip

The R script provides R code to regenerate the main paper Figures 2 to 7 modulo some visual modifications performed in Inkscape.

Provided R scripts are:

ComputePairWiseDE_v2.R (Required code for pairwise DE computation)

RCA_Figure_Reproduction.R

Provided python Code for Scanpy analysis:

RA_Scanpy.ipynb

CITESeq_Scanpy.ipynb

This repository contains code and processed scRNA-seq data of lamprey, which constructed a comprehensive cell atlas comprising 604,460 cells/nuclei and 70 cell types from 14 tissues.

lamprey_atlas.raw.h5ad:

Python data set (.h5ad) containing raw counts matrix from all tissues and libraries.

lamprey_atlas.scanpy_merge.h5ad:

Python data set (.h5ad) containing scanpy processed matrix, used in projection of cells from all tissues into shared UMAP space. Only highly-variable genes calculated by scanpy are included.

immune.h5ad:

Python data set (.h5ad) containing scanpy processed matrix, used in re-clustering of immune cells. Only highly-variable genes calculated by scanpy are included.

pancreas.evo.rds:

R data set (.rds) containing integrated data of intestine, liver, pancreas from human and mouse, as well as intestine and liver from lamprey.

lamprey-single-cell-atlas-1.0.0.zip:

Code used in processing of scRNA-seq data.

Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the processed data used for the figures related to scRNAseq experiments. The objects contain normalized gene expression data, metadata including cells annotation and UMAP coordinates.

File Extension Specification:

APP23xPS45xTREM2-IPD_full.h5ad: This AnnData stores the data used for Figure 2 (all genotypes, logitudinal for FULL FRONTAL CORTEX) APP23xPS45xTREM2-IPD_Microglia.h5ad: This AnnData stores the data used for Figure 3 and S4 (all genotypes, logitudinal for MICROGLIA) APP23xPS45xTREM2-IPD_Oligodendrocytes.h5ad: This AnnData stores the data used for Figure S6 (all genotypes, logitudinal for OLIGODEDROCYTES) WT_TREM2-IPD.h5ad: This AnnData stores the data used for Figure S3 (Wild Type & TREM2-IPD genotypes, logitudinal for FULL FRONTAL CORTEX)

Preprint version of the Single-Cell Tumor Immune Atlas

This upload contains:

• TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas (317111 cells, RNA and integrated assays, PCA and UMAP reductions)
• TICAtlas.h5ad: an h5ad file with the whole Atlas (317111 cells, RNA assay, PCA and UMAP)
• TICAtlas_RNA.rds: an rds file containing a Seurat object of the whole Atlas but only the RNA assay (317111 cells, UMAP embedding)
• TICAtlas_downsampled_1000.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA and integrated assay, PCA and UMAP reductions)
• TICAtlas_downsampled_1000.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA assay, PCA and UMAP reductions)
• TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells

For the h5ad files, the .X slot contains the normalized data, while the .X.raw slot contains the raw counts as they were in the original datasets.

All the files contain the following patient/sample metadata variables:

• patient: assigned patient identifiers
• gender: the patient's gender (male/female/unknown)
• source: dataset of origin
• subtype: cancer type (abbreviations as indicated in the preprint)
• cluster_kmeans_k6: patients clusters, NA if filtered out
• cell_type: annotated cell type for each of the cells

If you have any issues with the metadata you can use the TICAtlas_metadata.csv file.

For more information, read our preprint and check our GitHub.

h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.

Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.

The three datasets used as the basis for these embeddings were:

sample "Brain8" from the Jiang et al. 2021 zebrafish cell atlas (files beginning with GSM3768152)

sample "Brain1" from the Han et al. 2018 mouse cell atlas (files beginning with GSM2906405)

sample "Xenopus_brain_COL65" from the Liao et al. 2022 Xenopus laevis adult cell atlas (files beginning with GSM6214268)

For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.

For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").

For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy.

For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"

These data are part of the Arcadia Science Pub titled "Comparing gene expression across species based on protein structure instead of sequence".

The helper-like ILC contains various functional subsets, such as ILC1, ILC2, ILC3 and LTi cells, mediating the immune responses against viruses, parasites, and extracellular bacteria, respectively. Among them, LTi cells are also crucial for the formation of peripheral lymphoid tissues, such as lymph nodes. Our research, along with others’, indicates a high proportion of LTi cells in the fetal ILC pool, which significantly decreases after birth. Conversely, the proportion of non-LTi ILCs increases postnatally, corresponding to the need for LTi cells to mediate lymphoid tissue formation during fetal stages and other ILC subsets to combat diverse pathogen infections postnatally. However, the regulatory mechanism for this transition remains unclear. In this study, we observed a preference for fetal ILC progenitors to differentiate into LTi cells, while postnatal bone marrow ILC progenitors preferentially differentiate into non-LTi ILCs. Particularly, this differentiation shift occurs within the first week after birth in mice. Further analysis revealed that adult ILC progenitors exhibit stronger activation of the Notch signaling pathway compared to fetal counterparts, accompanied by elevated Gata3 expression and decreased Rorc expression, leading to a transition from fetal LTi cell-dominant states to adult non-LTi ILC-dominant states. This study suggests that the body can regulate ILC development by modulating the activation level of the Notch signaling pathway, thereby acquiring different ILC subsets to accommodate the varying demands within the body at different developmental stages.Data usageimport scanpy as sc# read the data using scanpyadata= sc.read_h5ad('./220516-ABM.velo.h5ad')# draw umap for visualization. `ann0608` is the cell type label.sc.pl.umap(adata,color='ann0608')# get gene expression matrixadata.X

The muscle stem cell (MuSC) population is recognized as functionally heterogeneous. Cranial muscle stem cells, which originate from head mesoderm, can have greater proliferative capacity in culture and higher regenerative potential in transplantation assays when compared to those in the limb. The existence of such functional differences in phenotypic outputs remain unresolved as a comprehensive understanding of the underlying mechanisms is lacking. We addressed this issue using a combination of clonal analysis, live imaging, and scRNA-seq, identifying critical biological features that distinguish extraocular (EOM) and limb (Tibialis anterior, TA) MuSC populations. Time-lapse studies using a MyogenintdTomato reporter showed that the increased proliferation capacity of EOM MuSCs is accompanied by a differentiation delay in vitro. Unexpectedly, in vitro activated EOM MuSCs expressed a large array of distinct extracellular matrix (ECM) components, growth factors, and signaling molecules that are typically associated with mesenchymal non-muscle cells. These unique features are regulated by a specific set of transcription factors that constitute a coregulating module. This transcription factor network, which includes Foxc1 as one of the major players, appears to be hardwired to EOM identity as it is present in quiescent adult MuSCs, in the activated counterparts during growth and retained upon passages in vitro. These findings provide insights into how high-performing MuSCs regulate myogenic commitment by active remodeling of their local environment. Methods

scRNAseq data generation MuSCs were isolated on BD FACSAriaTM III based on GFP fluorescence and cell viability from Tg:Pax7- nGFP mice (Sambasivan et al., 2009). Quiescent MuSCs were manually counted using a hemocytometer and immediately processed for scRNA-seq. For activated samples, MuSCs were cultured in vitro as described above for four days. Activated MuSCs were subsequently trypsinized and washed in DMEM/F12 2% FBS. Live cells were re-sorted, manually counted using a hemocytometer and processed for scRNA-seq. Prior to scRNAseq, RNA integrity was assessed using Agilent Bioanalyzer 2100 to validate the isolation protocol (RIN>8 was considered acceptable). 10X Genomics Chromium microfluidic chips were loaded with around 9000 cells and cDNA libraries were generated following manufacturer’s protocol. Concentrations and fragment sizes were determined using Agilent Bioanalyzer and Invitrogen Qubit. cDNA libraries were sequenced using NextSeq 500 and High Output v2.5 (75 cycles) kits. Count matrices were subsequently generated following 10X Genomics Cell Ranger pipeline. Following normalisation and quality control, we obtained an average of 5792 ± 1415 cells/condition. Seurat preprocessing scRNAseq datasets were processed using Seurat (https://satijalab.org/seurat/) (Butler et al., 2018). Cells with more than 10% of mitochondrial gene fraction were discarded. 4000-5000 genes were detected on average across all 4 datasets. Dimensionality reduction and UMAPs were generated following Seurat workflow. The top 100 DEGs were determined using Seurat "FindAllMarkers" function with default parameters. When processed independently (scvelo), the datasets were first regressed on cell cycle genes, mitochondrial fraction, number of genes, number of UMI following Seurat dedicated vignette, and doublets were removed using DoubletFinder v3 (McGinnis et al., 2019). A "StressIndex" score was generated for each cell based on the list of stress genes previously reported (Machado et al., 2021) using the “AddModule” Seurat function. 94 out of 98 genes were detected in the combined datasets. UMAPs were generated after 1. StressIndex regression, and 2. after complete removal of the detected stress genes from the gene expression matrix before normalization. In both cases, the overall aspect of the UMAP did not change significantly (Figure S5). Although immeasurable confounding effects of cell stress following isolation cannot be ruled out, we reasoned that our datasets did not show a significant effect of stress with respect to the conclusions of our study. Matrisome analysis After subsetting for the features of the Matrisome database (Naba et al., 2015) present in our single-cell dataset, the matrisome score was calculated by assessing the overall expression of its constituents using the "AddModuleScore" function from Seurat (Butler et al., 2018).

RNA velocity and driver genes Scvelo was used to calculate RNA velocities (Bergen et al., 2020). Unspliced and spliced transcript matrices were generated using velocyto (Manno et al., 2018) command line function. Seurat-generated filtering, annotations and cell-embeddings (UMAP, tSNE, PCA) were then added to the outputted objects. These datasets were then processed following scvelo online guide and documentation. Velocity was calculated based on the dynamical model (using scv.tl.recover_dynamics(adata), and scv.tl.velocity(adata, mode=’dynamical’)) and differential kinetics calculations were added to the model (using scv.tl.velocity(adata, diff_kinetics=True)). Specific driver genes were identified by determining the top likelihood genes in the selected cluster. The lists of the top 100 drivers for EOM and TA progenitors are given in Suppl Tables 10 and 11. Gene regulatory network inference and transcription factor modules Gene regulatory networks were inferred using pySCENIC (Aibar et al., 2017; Sande et al., 2020). This algorithm regroups sets of correlated genes into regulons (i.e. a transcription factor and its targets) based on binding motifs and co-expression patterns. The top 35 regulons for each cluster were determined using scanpy "scanpy.tl.rank_genes_groups" function (method=t-test). Note that this function can yield less than 35 results depending on the cluster. UMAP and heatmap were generated using regulon AUC matrix (Area Under Curve) which refers to the activity level of each regulon in a given cell. Visualizations were performed using scanpy (Wolf et al., 2018). The outputted list of each regulon and their targets was subsequently used to create a transcription factor network. To do so, only genes that are regulons themselves were kept. This results in a visual representation where each node is an active transcription factor and each edge is an inferred regulation between 2 transcription factors. When placed in a force-directed environment, these nodes aggregate based on the number of shared edges. This operation greatly reduced the number of genes involved, while highlighting co-regulating transcriptional modules. Visualization of this network was performed in a force-directed graph using Gephi “Force-Atlas2” algorithm (https://gephi.org/).

MCA2.0_Fig1_adata.h5ad: contains both raw count and normalized datasets for 520,801 cells in MCDA, which can be processed into the python environment with SCANPY directly. dge_raw_data.zip: contains separate raw count matrices for ten mouse tissues (brain, heart, lung, liver, kidney, stomach, intestine, pancreas, testis, and uterus) at seven life stages (E10.5Day, E12.5Day, E14.5Day, P0, P10, P21, and Adult). MCA2.0_cell_info.csv: contains the cell annotations for 520801 cells in MCDA, including UMAP coordinates, development stages, tissue origins, lineages, and cell-type annotations in both 95 clusters and tissues per stage.

This repository contains the preprocessed single-cell RNA data from the manuscript Transcriptomics of Alveolar Immune Cells Reveals Insight into Mechanisms of Human Pulmonary Fibrosis (under submission).

Data Descriptions: