ObjectiveTo guide animal experiments, we investigated the similarities and differences between humans and mice in aging and Alzheimer’s disease (AD) at the single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing (scRNA-seq) level.MethodsMicroglia cells were extracted from dataset GSE198323 of human post-mortem hippocampus. The distributions and proportions of microglia subpopulation cell numbers related to AD or age were compared. This comparison was done between GSE198323 for humans and GSE127892 for mice, respectively. The Seurat R package and harmony R package were used for data analysis and batch effect correction. Differentially expressed genes (DEGs) were identified by FindMarkers function with MAST test. Comparative analyses were conducted on shared genes in DEGs associated with age and AD. The analyses were done between human and mouse using various bioinformatics techniques. The analysis of genes in DEGs related to age was conducted. Similarly, the analysis of genes in DEGs related to AD was performed. Cross-species analyses were conducted using orthologous genes. Comparative analyses of pseudotime between humans and mice were performed using Monocle2.Results(1) Similarities: The proportion of microglial subpopulation Cell_APOE/Apoe shows consistent trends, whether in AD or normal control (NC) groups in both humans and mice. The proportion of Cell_CX3CR1/Cx3cr1, representing homeostatic microglia, remains stable with age in NC groups across species. Tuberculosis and Fc gamma R-mediated phagocytosis pathways are shared in microglia responses to age and AD across species, respectively. (2) Differences: IL1RAPL1 and SPP1 as marker genes are more identifiable in human microglia compared to their mouse counterparts. Most genes of DEGs associated with age or AD exhibit different trends between humans and mice. Pseudotime analyses demonstrate varying cell density trends in microglial subpopulations, depending on age or AD across species.ConclusionsMouse Apoe and Cell_Apoe maybe serve as proxies for studying human AD, while Cx3cr1 and Cell_Cx3cr1 are suitable for human aging studies. However, AD mouse models (App_NL_G_F) have limitations in studying human genes like IL1RAPL1 and SPP1 related to AD. Thus, mouse models cannot fully replace human samples for AD and aging research.

Includes V1 seurat objects used for input (regression with no scaling) and resulting monocle object.merge_meso_vsm_seur_ob - E12.5, E14.5, E17.5 V1 Dataset. Subclustered mesenchymal dataset for mesothelial and VSM populations. Prepared for monocle input by regression and no scaling analysis. Input for monocle_v1_seurat_input.R. merge_meso_vsm_monocle_ob - Resulting monocle object generated from merge_meso_vsm_seur_ob. Used as input for monocle_object_analysis.R. Grouped by "final_clus" in phenoData. Corresponds to Fig. 3e.

The attached R Scripts supplement our protocol paper currently under editorial review at the Journal of Visualized Experiments.Scope of the article:This protocol describes the general processes and quality control checks necessary for preparing healthy adult single cells in preparation for droplet-based, high-throughput single cell RNA-Seq analysis using the 10X Genomics' Chromium System. We also describe sequencing parameters, alignment and downstream single-cell bioinformatic analysis.

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`

The distal region of the uterine (Fallopian) tube is commonly associated with high-grade serous carcinoma (HGSC), the predominant and most aggressive form of ovarian or extra-uterine cancer. Specific cell states and lineage dynamics of the adult tubal epithelium (TE) remain insufficiently understood, hindering efforts to determine the cell of origin for HGSC. Here, we report a comprehensive census of cell types and states of the mouse uterine tube. We show that distal TE cells expressing the stem/progenitor cell marker Slc1a3 can differentiate into both secretory (Ovgp1+) and ciliated (Fam183b+) cells. Inactivation of Trp53 and Rb1, whose pathways are commonly altered in HGSC, leads to elimination of targeted Slc1a3+ cells by apoptosis, thereby preventing their malignant transformation. In contrast, pre-ciliated cells (Krt5+, Prom1+, Trp73+) remain cancer-prone and give rise to serous tubal intraepithelial carcinomas and overt HGSC. These findings identify transitional pre-ciliated cells as a previously unrecognized cancer-prone cell state and point to pre-ciliation mechanisms as novel diagnostic and therapeutic targets. Methods

Single-cell RNA-sequencing library preparation For TE single cell expression and transcriptome analysis we isolated TE from C57BL6 adult estrous female mice. In 3 independent experiments a total of 62 uterine tubes were collected. Each uterine tube was placed in sterile PBS containing 100 IU ml-1 of penicillin and 100 µg ml-1 streptomycin (Corning, 30-002-Cl), and separated in distal and proximal regions. Tissues from the same region were combined in a 40 µl drop of the same PBS solution, cut open lengthwise, and minced into 1.5-2.5 mm pieces with 25G needles. Minced tissues were transferred with help of a sterile wide bore 200 µl pipette tip into a 1.8 ml cryo vial containing 1.2 ml A-mTE-D1 (300 IU ml-1 collagenase IV mixed with 100 IU ml-1 hyaluronidase; Stem Cell Technologies, 07912, in DMEM Ham’s F12, Hyclone, SH30023.FS). Tissues were incubated with loose cap for 1 h at 37°C in a 5% CO2 incubator. During the incubation tubes were taken out 4 times and tissues suspended with a wide bore 200 µl pipette tip. At the end of incubation, the tissue-cell suspension from each tube was transferred into 1 ml TrypLE (Invitrogen, 12604013) pre-warmed to 37°C, suspended 70 times with a 1000 µl pipette tip, 5 ml A-SM [DMEM Ham’s F12 containing 2% fetal bovine serum (FBS)] were added to the mix, and TE cells were pelleted by centrifugation 300x g for 10 minutes at 25°C. Pellets were then suspended with 1 ml pre-warmed to 37°C A-mTE-D2 (7 mg ml-1 Dispase II, Worthington NPRO2, and 10 µg ml-1 Deoxyribonuclease I, Stem Cell Technologies, 07900), and mixed 70 times with a 1000 µl pipette tip. 5 ml A-mTE-D2 was added and samples were passed through a 40 µm cell strainer, and pelleted by centrifugation at 300x g for 7 minutes at +4°C. Pellets were suspended in 100 µl microbeads per 107 total cells or fewer, and dead cells were removed with the Dead Cell Removal Kit (Miltenyi Biotec, 130-090-101) according to the manufacturer’s protocol. Pelleted live cell fractions were collected in 1.5 ml low binding centrifuge tubes, kept on ice, and suspended in ice cold 50 µl A-Ri-Buffer (5% FBS, 1% GlutaMAX-I, Invitrogen, 35050-079, 9 µM Y-27632, Millipore, 688000, and 100 IU ml-1 penicillin 100 μg ml-1 streptomycin in DMEM Ham’s F12). Cell aliquots were stained with trypan blue for live and dead cell calculation. Live cell preparations with a target cell recovery of 5,000-6,000 were loaded on Chromium controller (10X Genomics, Single Cell 3’ v2 chemistry) to perform single cell partitioning and barcoding using the microfluidic platform device. After preparation of barcoded, next-generation sequencing cDNA libraries samples were sequenced on Illumina NextSeq500 System.

Download and alignment of single-cell RNA sequencing data For sequence alignment, a custom reference for mm39 was built using the cellranger (v6.1.2, 10x Genomics) mkref function. The mm39.fa soft-masked assembly sequence and the mm39.ncbiRefSeq.gtf (release 109) genome annotation last updated 2020-10-27 were used to form the custom reference. The raw sequencing reads were aligned to the custom reference and quantified using the cellranger count function.

Preprocessing and batch correction All preprocessing and data analysis was conducted in R (v.4.1.1 (2021-08-10)). The cellranger count outs were first modified with the autoEstCont and adjustCounts functions from SoupX (v.1.6.1) to output a corrected matrix with the ambient RNA signal (soup) removed (https://github.com/constantAmateur/SoupX). To preprocess the corrected matrices, the Seurat (v.4.1.1) NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, and RunUMAP functions were used to create a Seurat object for each sample (https://github.com/satijalab/seurat). The number of principal components used to construct a shared nearest-neighbor graph were chosen to account for 95% of the total variance. To detect possible doublets, we used the package DoubletFinder (v.2.0.3) with inputs specific to each Seurat object. DoubletFinder creates artificial doublets and calculates the proportion of artificial k nearest neighbors (pANN) for each cell from a merged dataset of the artificial and actual data. To maximize DoubletFinder’s predictive power, mean-variance normalized bimodality coefficient (BCMVN) was used to determine the optimal pK value for each dataset. To establish a threshold for pANN values to distinguish between singlets and doublets, the estimated multiplet rates for each sample were calculated by interpolating between the target cell recovery values according to the 10x Chromium user manual. Homotypic doublets were identified using unannotated Seurat clusters in each dataset with the modelHomotypic function. After doublets were identified, all distal and proximal samples were merged separately. Cells with greater than 30% mitochondrial genes, cells with fewer than 750 nCount RNA, and cells with fewer than 200 nFeature RNA were removed from the merged datasets. To correct for any batch defects between sample runs, we used the harmony (v.0.1.0) integration method (github.com/immunogenomics/harmony).

Clustering parameters and annotations After merging the datasets and batch-correction, the dimensions reflecting 95% of the total variance were input into Seurat’s FindNeighbors function with a k.param of 70. Louvain clustering was then conducted using Seurat’s FindClusters with a resolution of 0.7. The resulting 19 clusters were annotated based on the expression of canonical genes and the results of differential gene expression (Wilcoxon Rank Sum test) analysis. One cluster expressing lymphatic and epithelial markers was omitted from later analysis as it only contained 2 cells suspected to be doublets. To better understand the epithelial populations, we reclustered 6 epithelial populations and reapplied harmony batch correction. The clustering parameters from FindNeighbors was a k.param of 50, and a resolution of 0.7 was used for FindClusters. The resulting 9 clusters within the epithelial subset were further annotated using differential expression analysis and canonical markers.

Pseudotime analysis Potential of heat diffusion for affinity-based transition embedding (PHATE) is dimensional reduction method to more accurately visualize continual progressions found in biological data 35. A modified version of Seurat (v4.1.1) was developed to include the ‘RunPHATE’ function for converting a Seurat Object to a PHATE embedding. This was built on the phateR package (v.1.0.7) (https://github.com/scottgigante/seurat/tree/patch/add-PHATE-again). In addition to PHATE, pseudotime values were calculated with Monocle3 (v.1.2.7), which computes trajectories with an origin set by the user 36,55–57. The origin was set to be a progenitor cell state confirmed with lineage tracing experiments. 35. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol 37, 1482–1492 (2019). doi:10.1038/s41587-019-0336-3 36. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). doi:10.1038/s41586-019-0969-x 55. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology 32, 381–386 (2014). doi:10.1038/nbt.2859 56. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nature Methods 14, 309–315 (2017). doi:10.1038/nmeth.4150 57. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14, 979–982 (2017). doi:10.1038/nmeth.4402

Includes V1 seurat objects used for input (regression with no scaling) and resulting monocle object.E14_endocrine_mon_input_seur_ob - E14.5 endocrine V1 Dataset. Prepared for monocle, input into monocle_v1_seurat_input.R E14_endocrine_monocle_ob - Resulting monocle object generated from E14_endocrine_mon_input_seur_ob. Grouped by "ordered_res1_5" in phenoData. Corresponds to Fig. 4g. merge_fev_endocrine_seur_ob - E12.5, E14.5, E17.5 V1 Dataset. Subclustered epithelial dataset for Fev+/Pax4+, FevHi/Chgb+, Alpha, Beta, Epsilon, and Delta populations. Prepared for monocle, input into monocle_v1_seurat_input.R merge_fev_endocrine_monocle_ob - Resulting monocle object generated from merge_fev_endocrine_seur_ob. Grouped by "merged_clus" in phenoData. Corresponds to Supplementary Fig. 8a.

seurat v3 object

ASSAYS: AB: Antibody expression data RNA: mRNA expression data

DIMENSIONALITY REDUCTION Projected: Data was projected on the reference dataset MOFAUMAP coordinates (Triana et al., 2021) scanorama: Data was integrated with Scanorama, using the cohort as Batch key umap: umap computed from Scanorama components

METADATA patient: Patient cohort: Cohort day: Day of sampling (relevant for Cohort B only) ct: Projected cell type (Triana et al., 2021) ct_simple: Simplified projected celltype pseudo_myel: Projected myeloid pseudotime (Triana et al., 2021) projection_score: Score assessing the quality of projection (Triana et al 2021) id: Unsupervised clustering result leukemia_prob: CloneTracer leukemia probability status: Binarized status (healthy, leukemic, unsure)

This is the additional supporting data for the manuscript titled as "Recent evolution of the developing human intestine affects metabolic and barrier functions". It contains 1) comprehensive annotation of open chromatin regions detected in developing human or chimp intestine organoids or tissue; 2) cell type enriched features, including (A) developing human proximal small intestine tissue cell type marker genes and regions, (B) in vitro and transplanted HIO cell type enriched genes and regions, (C) in vitro and transplanted CIO cell type enriched genes and regions, (D) developing mouse epithelial cell type marker genes; 3) amino acid substitution rate (dN/dS) and epithelial cell type expression specificity (Tau) of epithelial cell class and specific cell type marker genes; 4) differentially expressed genes (DEG) between species, including (A) human-chimp DEGs of each intestinal epithelial cell type of in vitro or transplanted organoids, (B) human-mouse DEGs of epithelial cell type between developing human and mouse tissues, (C) developing human and mouse tissue DEG along stem-cell-to-enterocyte pseudotime trajectory, (D) human-chimp DEGs based on consensus genome; (E) human-chimp DEGs after stem cell to enterocyte differentiation pseudotime alignment; 5) collected evolutionary selection signatures for composite annotation of open chromatin regions 6) Seurat objects of scSTARR-seq experiments and the table for the calculation of the normalized CRE activity 7) Seurat objects of human, chimpanzee, mouse data 8) Human, chimpanzee, and mouse stem cell to enterocyte pseudotime (Pt) aligned expression matrix

Skeletal muscle regeneration relies on the orchestrated interaction of myogenic and non-myogenic cells with spatial and temporal coordination. The regenerative capacity of skeletal muscle declines with aging due to alterations in myogenic stem/progenitor cell states and functions, non-myogenic cell contributions, and systemic changes, all of which accrue with age. A holistic network-level view of the cell-intrinsic and -extrinsic changes influencing muscle stem/progenitor cell contributions to muscle regeneration across the lifespan remains poorly resolved. To provide a comprehensive atlas of regenerative muscle cell states across mouse lifespan, we collected a compendium of 273,923 single-cell transcriptomes from hindlimb muscles of young, old, and geriatric (4-7, 20, and 26 months old, respectively) mice at six closely sampled time-points following myotoxin injury. We identified 29 muscle-resident cell types, eight of which exhibited accelerated or delayed dynamics in their abundances between age groups, including T and NK cells and multiple macrophage subtypes, suggesting that the age-related decline in muscle repair may arise from temporal miscoordination of the inflammatory response. We performed a pseudotime analysis of myogenic cells across the regeneration timespan and found age-specific myogenic stem/progenitor cell trajectories in old and geriatric muscles. Given the critical role that cellular senescence plays in limiting cell contributions in aged tissues, we built a series of tools to bioinformatically identify senescence in these single-cell data and assess their ability to identify senescence within key myogenic stages. By comparing single-cell senescence scores to co-expression of hallmark senescence genes Cdkn2a and Cdkn1a, we found that an experimentally derived gene list derived from a muscle foreign body response (FBR) fibrosis model accurately (receiver-operator curve AUC = 0.82-0.86) identified senescent-like myogenic cells across mouse ages, injury time-points, and cell-cycle states, in a manner comparable to curated gene-lists. Further, this scoring approach in both single-cell and spatial transcriptomic datasets pinpointed transitory senescent-like subsets within the myogenic stem/progenitor cell trajectory that are associated with stalled MuSC self-renewal states across all ages of mice. This new resource on mouse skeletal muscle aging provides a comprehensive portrait of the changing cellular states and interactions underlying skeletal muscle regeneration across the mouse lifespan. Methods Mouse muscle injury and single-cell isolation. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols (approval # 2014-0085), and experiments were performed in compliance with its institutional guidelines. Mice were maintained at 70-73°F on a 14/10-h light/dark with humidity mainly at 40%. Muscle injury was induced in young (4-7 months-old [mo]), old (20 mo), and geriatric (26 mo) C57BL/6J mice (Jackson Laboratory # 000664; NIA Aged Rodent Colonies) by injecting both tibialis anterior (TA) muscles with 10 µl of notexin (10 µg/ml; Latoxan, France). The mice were sacrificed, and TA muscles were collected at 0, 1, 2, 3.5, 5, and 7 days post-injury (dpi) with n = 3-4 biological replicates per sample. Each TA was processed independently to generate single-cell suspensions. At each time point, the young and old samples are biological replicates of TA muscles from distinct mice, and the geriatric samples are biological replicates of two TA muscles from each of the two mice. A mixture of male and female mice was used. See Supplemental Table 1 for additional details. Muscles were digested with 8 mg/ml Collagenase D (Roche, Basel, Switzerland) and 10 U/ml Dispase II (Roche, Basel, Switzerland) and then manually dissociated to generate cell suspensions. Myofiber debris was removed by filtering the cell suspensions through a 100 µm and then a 40 µm filter (Corning Cellgro # 431752 and # 431750). After filtration, erythrocytes were removed by incubating the cell suspension inan erythrocyte lysis buffer (IBI Scientific # 89135-030). Single-cell RNA-sequencing library preparation. After digestion, the single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. A hemocytometer was used to manually count the cells to determine the concentration of the suspension. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, Pleasanton, CA) following the manufacturer’s protocol (10x Genomics: Resolving Biology to Advance Human Health, 2020). Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes with <5% doublet rate. Libraries were sequenced on the NextSeq 500 (Illumina, San Diego, CA) (Illumina | Sequencing and array-based solutions for genetic research, 2020). The sequencing data was aligned to the mouse reference genome (mm10) using CellRanger v5.0.0 (10x Genomics) (10x Genomics: Resolving Biology to Advance Human Health, 2020). Preprocessing single-cell RNA-sequencing data. From the gene expression matrix, the downstream analysis was carried out in R (v3.6.1). First, the ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX) (Young and Behjati, 2020). Samples were then preprocessed using the standard Seurat (v3.2.3) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat) (Stuart et al., 2019). Cells with fewer than 200 genes, with fewer than 750 UMIs, and more than 25% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0.3) was used to identify putative doublets in each dataset (McGinnis, Murrow, and Gartner, 2019). The estimated doublet rate was 5% according to the 10x Chromium handbook. The putative doublets were removed from each dataset. Next, the datasets were merged and then batch-corrected with Harmony (github.com/immunogenomics/harmony) (v1.0) (Korsunsky et al., 2019). Seurat was then used to process the integrated data. Dimensions accounting for 95% of the total variance were used to generate SNN graphs (FindNeighbors) and SNN clustering was performed (FindClusters). A clustering resolution of 0.8 was used resulting in 24 initial clusters. Cell type annotation in single-cell RNA-sequencing data. Cell types were determined by expression of canonical genes. Each of the 24 initial clusters received a unique cell type annotation. The nine myeloid clusters were challenging to differentiate between, so these clusters were subset out (Subset) and re-clustered using a resolution of 0.5 (FindNeighbors, FindClusters) resulting in 15 initial clusters. More specific myeloid cell type annotations were assigned based on the expression of canonical myeloid genes. This did not help to clarify the monocyte and macrophage annotations, but it did help to identify more specific dendritic cell and T cell subtypes. These more specific annotations were transferred from the myeloid subset back to the complete integrated object based on the cell barcode. Analysis of cell type dynamics. We generated a table with the number of cells from each sample (n = 65) in each cell type annotation (n = 29). We removed the erythrocytes from this analysis because they are not a native cell type in skeletal muscle. Next, for each sample, we calculated the percent of cells in each cell type annotation. The mean and standard deviation were calculated from each age and time point for every cell type. The solid line is the mean percentage of the given cell type, the ribbon is the standard deviation around the mean, and the points are the values from individual replicates. We evaluated whether there was a significant difference in the cell type dynamics over all six-time points using non-linear modeling. The dynamics for each cell type were fit to some non-linear equation (e.g., quadratic, cubic, quartic) independent and dependent on age. The type of equation used for each cell type was selected based on the confidence interval and significance (p < 0.05) of the leading coefficient. If the leading coefficient was significantly different from zero, it was concluded that the leading coefficient was needed. If the leading coefficient was not significantly different than zero, it was concluded that the leading coefficient was not needed, and the degree of the equation went down one. No modeling equation went below the second degree. The null hypothesis predicted that the coefficients of the non-linear equation were the same across the age groups while the alternative hypothesis predicted that the coefficients of the non-linear equation were different across the age groups. We conducted a One-Way ANOVA to see if the alternative hypothesis fits the data significantly better than the null hypothesis and we used FDR as the multiple comparison test correction (using the ANOVA and p.adjust (method = fdr) functions in R, respectively). T cell exhaustion scoring. We grouped the three T cell populations (this includes Cd3e+ cycling and non-cycling T cells and Cd4+ T cells) and z-scored all genes. The T cell exhaustion score was calculated using a transfer-learning method developed by Cherry et al 2023 and a T cell exhaustion gene list from Bengsch et al 2018 (Bengsch et al., 2018; Cherry et al., 2023). The Mann-Whitney U-test was performed on the T cell exhaustion score between ages. Senescence scoring. We tested two senescence-scoring methods along with fourteen senescence gene lists (Supplemental Table 2) to identify senescent-like cells within the scRNA-seq dataset. The Two-way Senescence Score (Sen Score) was calculated using a transfer-learning method developed by Cherry et al 2023 (Cherry et al., 2023). With this

This is the additional supporting data for the manuscript titled as "Recent evolution of the developing human intestine impacts metabolic and barrier functions". It contains 1) comprehensive annotation of open chromatin regions detected in developing human or chimp intestine organoids or tissue; 2) cell type enriched features, including (A) developing human proximal small intestine tissue cell type marker genes and regions, (B) in vitro and transplanted HIO cell type enriched genes and regions, (C) in vitro and transplanted CIO cell type enriched genes and regions, (D) developing mouse epithelial cell type marker genes; 3) amino acid substitution rate (dN/dS) and epithelial cell type expression specificity (Tau) of epithelial cell class and specific cell type marker genes; 4) differentially expressed genes (DEG) between species, including (A) human-chimp DEGs of each intestinal epithelial cell type of in vitro or transplanted organoids, (B) human-mouse DEGs of epithelial cell type between developing human and mouse tissues, (C) developing human and mouse tissue DEG along stem-cell-to-enterocyte pseudotime trajectory, (D) human-chimp DEGs based on consensus genome; (E) human-chimp DEGs after stem cell to enterocyte differentiation pseudotime alignment; 5) collected evolutionary selection signatures for composite annotation of open chromatin regions 6) Seurat objects of scSTARR-seq experiments and the table for the calculation of the normalized CRE activity 7) Seurat objects of human, chimpanzee, mouse data 8) Human, chimpanzee, and mouse stem cell to enterocyte pseudotime (Pt) aligned expression matrix

seurat v3 object

ASSAYS: RNA: mRNA expression data

DIMENSIONALITY REDUCTION projected: Data was projected on the main AML dataset from Cohorts A and B. scanorama: Data was integrated with Scanorama, using the patient as Batch key umap: umap computed from Scanorama components

METADATA patient: Patient ct: Projected cell type (Triana et al., 2021) ct_simple: Simplified projected celltype pseudo_myel: Projected myeloid pseudotime (Triana et al., 2021) proj_cluster: Projected cluster from main AML dataset dormancy_score: score assessing the dormancy of cells based on Zhang 2021 and Cabezas-Wallscheid 2017 gene lists.

BackgroundThe immune microenvironment plays a critical role in maintaining skin homeostasis, which is closely related to the dysfunction in photoaged skin such as autoimmunity and tumorigenesis. Several recent studies have demonstrated the efficacy of 5-aminolevulinic acid photodynamic therapy (ALA-PDT) in alleviating photoaging and skin cancer. However, the underlying immune mechanisms and the immune microenvironment change by ALA-PDT remain largely unknown.MethodsTo illustrate the effects of ALA-PDT on immune microenvironment in photoaged skin, single cell RNA sequencing (scRNA-seq) analysis of photoaged skin on the extensor side of the human forearm before and after ALA-PDT was performed. R-packages of Seurat, clusterProfiler, Monocle, CellChat were used for cell clustering, differentially expressed genes analysis, functional annotation, pseudotime analysis and cell-cell communication analysis. The gene sets related to specific functions were extracted from the MSigDB database, which were used to score the functions of immune cells in different states. We also compared our result with published scRNA-seq data of photoaged skin of the eyelids.ResultsThe increase score of cellular senescence, hypoxia and reactive oxygen species pathway in immune cells and the decrease of immune receptor activity function and proportion of naive T cells were found in skin photoaging. Moreover, the function of T cell ribosomal synthesis was also impaired or down regulated and function of G2M checkpoint was up regulated. However, ALA-PDT showed promising results in reversing these effects, as it improved the above functions of T cells. The ratio of M1/M2 and percentage of Langerhans cells also decreased with photoaging and increased after ALA-PDT. Additionally, ALA-PDT restored the antigen presentation and migration function of dendritic cells and enhanced cell-cell communication among immune cells. These effects were observed to last for 6 months.ConclusionALA-PDT has potential to rejuvenate immune cells, partially reversed immunosenescence and improved the immunosuppressive state, ultimately remodelling the immune microenvironment in photoaged skin. These results provide an important immunological basis for further exploring strategies to reverse skin photoaging, chronological aging and potentially systemic aging.

Background In recent years, studies have demonstrated that neurogenesis can also occur in the adult mammal hypothalamus. Although the hypothalamus is a critical brain region that plays a vital role in regulating homeostatic and survival-related behaviors, there is still limited knowledge about its intrinsic mechanisms of development.

Our Goal Our goal is to identify and extensively characterize the cell-type-specific features during neurogenesis in the hypothalamic region of mice, from postnatal to young adult stages. We processed and analyzed publicly available scRNA-seq transcriptomic data that were obtained from the hypothalamic regions of mice, using uniform and optimized informatics pipeline.

Methods We obtained 10 scRNA-seq datasets from independent studies using from the NCBI GEO, which were further processed using the Seurat package in R (v4.2.1). A standard informatics pipeline was applied to each dataset for pre-processing and cell clustering for cell-level metadata standardization (See R script). Further, Pseudotime trajectory using the Monocle3 Alpha package in R (v. 2.99.1) and slingshot package in R (v. 2.6.0) was performed to see developmental differentiation in the hypothalamus during ault neurogenesis. We also generated connect-seq-derived scRNA-seq dataset Validation.rds (barcodes.tsv.gz, features.tsv.gz, matrix.mtx.gz) of 1,533 cells from whole hypothalamus and the bed nucleus of the stria terminalis (BNST) for validation analysis of our integrated dataset, by performing anchor-based mapping and transferring of labels and merging both integrated and validation dataset(refquery.rds).

Result Our integrated dataset (final_hypo_ann.rds) has revealed 30 distinct cell types that encompass all major cell types found in the hypothalamic regions, including glial-like cells such as ependymal cells, tanycytes, astrocytes, oligodendrocytes, and intermediate progenitor cells (IPCs) and also explored gene expression and cellular differentiation in the hypothalamus across various stages of development.

Background: There are still residual risks for atherosclerosis (AS)-associated cardiovascular diseases to be resolved. Considering the vital role of phenotypic switching of smooth muscle cells (SMCs) in AS, especially in calcification, targeting SMC phenotypic modulation holds great promise for clinical implications.Methods: To perform an unbiased and systematic analysis of the molecular regulatory mechanism of phenotypic switching of SMCs during AS in mice, we searched and included several publicly available single-cell datasets from the GEO database, resulting in an inclusion of more than 80,000 cells. Algorithms implemented in the Seurat package were used for cell clustering and cell atlas depiction. The pySCENIC and SCENIC packages were used to identify master regulators of interested cell groups. Monocle2 was used to perform pseudotime analysis. clusterProfiler was used for Gene Ontology enrichment analysis.Results: After dimensionality reduction and clustering, reliable annotation was performed. Comparative analysis between cells from normal artery and AS lesions revealed that three clusters emerged as AS progression, designated as mSMC1, mSMC2, and mSMC3. Transcriptional and functional enrichment analysis established a continuous transitional mode of SMCs’ transdifferentiation to mSMCs, which is further supported by pseudotime analysis. A total of 237 regulons were identified with varying activity scores across cell types. A potential core regulatory network was constructed for SMC and mSMC subtypes. In addition, module analysis revealed a coordinate regulatory mode of regulons for a specific cell type. Intriguingly, consistent with gain of ossification-related transcriptional and functional characteristics, a corresponding small set of regulators contributing to osteochondral reprogramming was identified in mSMC3, including Dlx5, Sox9, and Runx2.Conclusion: Gene regulatory network inference indicates a hierarchical organization of regulatory modules that work together in fine-tuning cellular states. The analysis here provides a valuable resource that can provide guidance for subsequent biological experiments.

Shown are the 966 bulk RNA-seq samples used in this study, including: source (iPSCORE or GTEx); subject ID; assay ID; SRA run ID for the GTEx RNA-seq samples downloaded from dbGaP; iPSCORE iPSC line identifier submitted to dbGaP (phs001325), iPSCORE unique differentiation identifier (UDID) assigned to all molecular data generated from same iPSC-CVPC differentiation; total number of reads; % uniquely mapped reads; % of mitochondrial reads, calculated as the number of reads mapping to mitochondrial genes divided by the total number of reads mapping to genes; tissue and organ associated with each sample (arteria, heart, iPSC-CVPC, aorta, coronary artery, atrial appendage or left ventricle: 0 = absent; 1 = present); 50 principal components calculated on the expression of 2,000 genes across the 966 CVS samples using Seurat; UMAP coordinates of each sample after clustering by Seurat; S phase and G2M and scores calculated using Seurat; pseudotime score of each sample using Monocle; cluster membership of each sample calculated using Seurat at four different resolutions (Figs 1A and 1B, S2 and S3); estimated cell type proportions deconvoluted using CIBERSORT; the last column (“Trimmed”) indicates which iPSCORE samples had their read length trimmed to 75 bp to test whether different read lengths between iPSCORE and GTEx affect differential gene and isoform expression analyses. (CSV)

PurposeTo assess the diverse cell populations of human corpus cavernosum in patients with severe erectile dysfunction (ED) at the single-cell level.MethodsPenile tissues collected from three patients were subjected to single-cell RNA sequencing using the BD Rhapsody™ platform. Common bioinformatics tools were used to analyze cellular heterogeneity and gene expression profiles from generated raw data, including the packages Seurat, Monocle, and CellPhoneDB.ResultsDisease-related heterogeneity of cell types was determined in the cavernous tissue such as endothelial cells (ECs), smooth muscle cells, fibroblasts, and immune cells. Reclustering analysis of ECs identified an arteriole ECs subcluster and another one with gene signatures of fibroblasts. The proportion of fibroblasts was higher than the other cell populations and had the most significant cellular heterogeneity, in which a distinct subcluster co-expressed endothelial markers. The transition trajectory of differentiation from smooth muscle cells into fibroblasts was depicted using the pseudotime analysis, suggesting that the expansion of corpus cavernosum is possibly compromised as a result of fibrosis. Cell-cell communications among ECs, smooth muscle cells, fibroblasts, and macrophages were robust, which indicated that inflammation may also have a crucial role in the development of ED.ConclusionsOur study has demonstrated a comprehensive single-cell atlas of cellular components in human corpus cavernosum of ED, providing in-depth insights into the pathogenesis. Future research is warranted to explore disease-specific alterations for individualized treatment of ED.

ObjectiveCuproptosis is a newly discovered form of programmed cell death that has not been studied in pulmonary fibrosis. The purpose of the present study was to explore the relationship between cuproptosis and pulmonary fibrosis.MethodsSingle-cell sequencing (scRNA-seq) data for human and mouse pulmonary fibrosis were obtained online from Gene Expression Omnibus (GEO) database. First, fibroblast lineage was identified and extracted using the Seurat toolkit. The pathway was then evaluated via Gene Set Enrichment Analyses (GSEA), while transcription factor activity was analyzed using DoRothEA. Next, fibroblast differentiation trajectory was inferred via Monocle software and changes in gene expression patterns during fibroblast activation were explored through gene dynamics analysis. The trajectory was then divided into three cell states in pseudotime order and the expression level of genes related to cuproptosis promotion in each cell state was evaluated, in addition to genes related to copper export and buffering and key genes in cellular metabolic pathways.ResultsIn the mouse model of pulmonary fibrosis induced by bleomycin, the genes related to cuproptosis promotion, such as Fdx1, Lias, Dld, Pdha1, Pdhb, Dlat, and Lipt1, were gradually down-regulated in the process of fibroblast differentiation from resting fibroblast to myofibroblast. Consistently, the same results were obtained via analysis of scRNA-seq data for human pulmonary fibrosis. In addition, genes related to copper ion export and buffering gradually increased with the activation of fibroblasts. Metabolism reprogramming was also observed, while fibroblast activation and tricarboxylic acid(TCA) cycle and lipid metabolism were gradually down-regulated and mitochondrial metabolism was gradually up-regulated.ConclusionThe present study is the first to reveal a negative correlation between cuproptosis and fibrosis, suggesting that an appropriate cuproptosis level may be involved in inhibiting fibroblast activation. This may provide a new method for the treatment of pulmonary fibrosis.

BackgroundKnee osteoarthritis (KOA) is a chronic inflammatory joint disorder marked by cartilage degradation and immune microenvironment dysregulation. While transcriptomic studies have identified key pathways in KOA, the interplay between ferroptosis (an iron-dependent cell death mechanism) and immune dysfunction at single-cell resolution remains unexplored. This study integrates single-cell and bulk transcriptomics to dissect ferroptosis-driven immune remodeling and identify diagnostic biomarkers in KOA.MethodsWe analyzed scRNA-seq data (GSE255460, n = 11) and bulk RNA-seq cohorts (GSE114007: 20 KOA/18 controls; GSE246425: 8 KOA/4 controls). Single-cell data were processed via Seurat (QC: mitochondrial genes >3 MAD; normalization: LogNormalize; batch correction: Harmony) and annotated using CellMarker/PanglaoDB. CellChat decoded intercellular communication, SCENIC reconstructed transcriptional networks, and Monocle2 for pseudotime trajectory mapping. Immune infiltration (CIBERSORT) and a LASSO-SVM diagnostic model were validated by ROC curves. Functional enrichment (GSEA/GSVA) and immunometabolic profiling were performed.ResultsTwelve chondrocyte clusters were identified, including ferroptosis-active homeostasis chondrocytes (HomC) (p < 0.01), which exhibited 491 DEGs linked to lipid peroxidation. HomC orchestrated synovitis via FGF signaling (ligand-receptor pairs: FGF1-FGFR1), amplifying ECM degradation and inflammatory cascades (CellChat). SCENIC revealed 10 HomC-specific regulons (e.g., SREBF1, YY1) driving matrix metalloproteinase activation. A 7-gene diagnostic panel (IFT88, MIEF2, ABCC10, etc.) achieved AUC = 1.0 (training) and 0.78 (validation). Immune profiling showed reduced resting mast cells (p = 0.003) and monocytes (p = 0.02), with ABCC10 correlating negatively with CD8+ T cells (r = -0.65) and M1 macrophages. GSEA/GSVA implicated HIF-1, NF-κB, and oxidative phosphorylation pathways in KOA progression. Pseudotime analysis revealed fibrotic transitions (COL1A1↑, TNC↑) in late-stage KOA cells.ConclusionThis study establishes ferroptosis as one of the key drivers immune-metabolic dysfunction in KOA, with HomC acting as a hub for FGF-mediated synovitis and ECM remodeling. The diagnostic model and regulon network (SREBF1/YY1) offer translational tools for early detection, while impaired mast cell homeostasis highlights novel immunotherapeutic targets. Our findings bridge ferroptosis, immune dysregulation, and metabolic stress, advancing precision strategies for KOA management.

BackgroundResident phenotypic memory CD8+ T cells are crucial for immune defense against pathogens. However, little is known about the potential transitions and regulation mechanisms of their function after influenza virus infection and reinfection. In this study, we utilized integrated transcriptome data and in vivo experiments to investigate the key characteristics behind it.MethodsTwo single-cell RNA sequencing (scRNA-seq) datasets of lung CD8+ T cells and one RNA-seq dataset of lung tissue after infection or reinfection were included. After Seurat procedures classifying CD8+ T subsets, the scCODE algorithm was used to identify the differentially expressed genes for GSVA, GO, and KEGG pathway enrichment. Monocle 3 and CellChat were used to infer pseudotime cell trajectory and cell interactions. The ssGSEA method was used to estimate the relative proportions of immune cells. The findings were confirmed with a mouse model via flow cytometry and RT-PCR analysis.ResultsOur study refined the landscape of CD8+ T-cell subsets in the lung, showing that CD8+ Trm cells accumulated in the lung within 14 days after influenza infection. The classical CD8+ Trm cells co-expressed a high level of CD49a and even maintained 90 days after primary infection. The ratio of CD8+ Trm cells decreased 1 day after influenza reinfection, which may be parallel with their potential transition into effector types, as observed in trajectory inference analysis. KEGG analysis suggested that PD-L1 expression and PD-1 checkpoint pathway were upregulated in CD8+ Trm cells on day 14 after infection. GO and GSVA analyses revealed that PI3K-Akt-mTOR and type I interferon signaling pathways were enriched in CD8+ Tem and Trm cells after reinfection. Additionally, CCL signaling pathways were involved in cell interaction between CD8+ Trm cells and other cells, with Ccl4-Ccr5 and Ccl5-Ccr5 ligand/receptor pairs being important between CD8+ Trm and other memory subsets after infection and reinfection.ConclusionOur data suggest that resident memory CD8+ T cells with CD49a co-expression account for a large proportion after influenza infection, and they can be rapidly reactivated against reinfection. Function differences exist in CD8+ Trm and Tem cells after influenza infection and reinfection. Ccl5-Ccr5 ligand/receptor pair is important in cell interactions between CD8+ Trm and other subsets.

This is the dataset supporting the EPI-Clone manuscript: Targeted single cell methylation profiling of hematopoietic stem and progenitor cells (HSPCs) was performed with the scTAMseq method. Three experiments are included: In M.1 and M.2 HSCs were clonally labeled with the LARRY system, transplanted to recipient mouse and profiled 4-5 months later (post-transplant hematopoiesis). In M.3 HSPCs were profiled straight from an unperturbed mouse.Remaining experiments are available under different DOIs:Mature cell experiment: https://doi.org/10.6084/m9.figshare.25472467Unperturbed old vs young mouse experiment: https://doi.org/10.6084/m9.figshare.25472434Human total bone marrow: https://doi.org/10.6084/m9.figshare.25526899Dataset is a seurat (v4) object with the following assays, reductions and metadata:ASSAYS:AB: Antibody expression dataDNAm: DNA methylation data, containing binary observations (0: amplicon not observed, i.e. dropout or absence of DNA methylation, 1: amplicon observed, i.e. DNA methylation). See the paper on scTAMseqintegrated: Integration of DNA methylation data performed across experimental batches.DIMENSIONALITY REDUCTIONpca: PCA performed on the integrated dataumap: UMAP computed on the integrated dataFor strategies how to obtain dimensionality reduction that reflect clonal identity, please see the github page accompanying the manuscript.METADATAExperiment: The experiment that the cell is from, values are "LARRY main experiment", "LARRY replicate" and "Native hematopoiesis"ProcessingBatch: Experiments were processed in several batches.CellType: Cell type annotationLARRY: Error corrected LARRY barcodeLARRYSize: Size of the cloneGFP.or.Saphire: Identity of the donor mouse. For M.1, two donor mice were sacrificed and HSCs were labeled with LARRY constructs containing a GFP label in one case, and LARRY constructs containing a Sapphire label in the other case. Subsequently, labeled cells from each donor were transplanted into two recipient mice each. Accordingly, the data set contains cells from four mice that contain two sets of clones, labeled with GFP and Sapphire, respectively.Pseudotime: Differentiation pseudotimePerformanceNonHhaI: Performance of the control amplicons in that cell