Data to create figure 3: Comparing independent datasets using SCA pseudo-bulk. A) Pearson similarity matrix generated comparing RNA-5c (X1-5) and RNA-3c (Y1-4) clusters, together with the bulk cell lines transcriptome. Black arrows associate clusters on the basis higher similarity depicted among clusters. B) Seaurat integration table. On the colums are shown the integration clusters and on the rows the number of cells from each RNA-5c and RNA-3c cluster present in the integration clusters. C) UMAP plot of the Seurat integrated clusters. Cell line association is given by the hierarchical clustering shown in Figure 2. Y1 cell line association is indicated with a questin mark since by Figure 2D, Y1 seems to be associated to H1975, as instead by seurat integration and SCA pseudo bulk Y1 is more similar to HCC827 than to H1975Figure 3A somewhere_in_your_computer/fig3/psbulk_integration/old_psAE/c5c3.pngFigure 3Csomewhere_in_your_computer/fig3/seurat_integration/Rplots.pdf

This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation.

A summary of the content is provided in the following.

R scripts

Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R

Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R

RDS files

General scRNA-seq Seurat objects:

scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS

scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds

Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS

UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS

SCENIC files:

Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds

AUC values computed for regulons: 3.4_regulonAUC.Rds

MetaData used in SCENIC cellInfo.Rds

Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS

Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS

Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS

Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS

BCR-ABL1 inference:

HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS

UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS

HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS

NK sub-clustering and filtering:

NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS

Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR

NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS

NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS

txt and csv files:

Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt

Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt

Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt

GSEA results:

HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt

NK: NK_For_Plotting.txt

TFRC and HLA expression: TFRC_and_HLA_Values.txt

NATMI result files:

UP-regulated_mean.csv

DOWN-regulated_mean.csv

Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt

Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv

Compressed folders:

All CyTOF raw data files: CyTOF_Data_raw.zip

Results of the patient-based classifier: PatientwiseClassifier.zip

Results of the single-cell based classifier: SingleCellClassifierResults.zip

For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there.

RAW data is available at EGA upon request using Study ID: EGAS00001005509

Revision

The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.

##### CD40 activation and the effect on Neutrophils

# Load necessary libraries for data manipulation, analysis, and visualization

library(dplyr)

library(Seurat)

library(patchwork)

library(plyr)

# Set the working directory to the folder containing the data

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/938-1_cellranger_count/outs")

# Read the M0 dataset from the 10X Genomics format

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

RNA <- pbmc.data$`Gene Expression`

ADT <- pbmc.data$`Antibody Capture`

HST <- pbmc.data$`Multiplexing Capture`

# Load the Matrix package

library(Matrix)

# Hashtag 1, 2 and 3 are marking the organs (heart, blood, spleen)

# Subset the rows based on row names

subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")

animals_data <- HST[subsetted_rows, , drop = FALSE]

# Hashtag 4, 5, 6, 7 are representing IgG_1, IgG_1, FGK45_1 and FGK45_1

subsetted_rows <- c("TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")

treatment_data <- HST[subsetted_rows, , drop = FALSE]

#Create a Seurat obeject and more assays to combine later

RNA <- CreateSeuratObject(counts = RNA)

ADT <- CreateAssayObject(counts = ADT)

Organ <- CreateAssayObject(counts = animals_data)

Treatment <- CreateAssayObject(counts = treatment_data)

seurat <- RNA

#Add the Assays

seurat[["ADT"]] <- ADT

seurat[["HST_Mice"]] <- Organ

seurat[["HST_Treatment"]] <- Treatment

#Check for AK Names

rownames(seurat[["ADT"]])

#Cluster cells on the basis of their scRNA-seq profiles

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

FeaturePlot(seurat, features = "S100a9", order = T)

# Normalize ADT data,

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

#Demultiplex cells based on Mouse_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Mice_classification.global)

DimPlot(seurat, group.by = "HST_Mice_classification")

#Demultiplex cells based on Treatment_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Treatment_classification.global)

DimPlot(seurat, group.by = "HST_Treatment_classification")

Idents(seurat) <- seurat$HST_Treatment_classification

pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)

Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification

pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

#Redo the clssification to remove the doublettes

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)

table(pbmc.singlet$HST_Treatment_classification.global)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)

table(pbmc.singlet$HST_Mice_classification.global)

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")

seurat <- pbmc.singlet

seurat$organ <- seurat$HST_Mice_maxID

seurat$mouse <- seurat$HST_Treatment_maxID

seurat$treatment <- seurat$HST_Treatment_maxID

library(plyr)

seurat$treatment <- revalue(seurat$treatment, c(

"TotalSeq-B0304" = "IgG",

"TotalSeq-B0305" = "IgG",

"TotalSeq-B0306" = "FGK45",

"TotalSeq-B0307" = "FGK45"

))

library(plyr)

seurat$organ <- revalue(seurat$organ, c(

"TotalSeq-B0301" = "heart",

"TotalSeq-B0302" = "blood",

"TotalSeq-B0303" = "spleen"

))

seurat$mouse <- revalue(seurat$mouse, c(

"TotalSeq-B0304" = "1",

"TotalSeq-B0305" = "2",

"TotalSeq-B0306" = "3",

"TotalSeq-B0307" = "4"

))

#Cluster cells on the basis of their scRNA-seq profiles without doublettes

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/Analyse")

saveRDS(seurat, file = "FGK45_heart_blood_spleen.v0.1.RDS")

Integration Skript:

library(Seurat)
library(tidyverse)
library(Matrix)

#cite <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Merge AAA mit Cite AAA/Cite_seq_v0.41.rds")
#CD45 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/CD45.rds")
AAA <- readRDS("C:/Users/alex/sciebo/AAA_Zhao_v4.rds")
cite <- readRDS("C:/Users/alex/sciebo/CITE_Seq_v0.5.rds")
all4 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/Schrader_All4_Rohanalyse/all4_220228.rds")

#fuse lists
c <- list(cite, all4, AAA)
names(c) <- c("cite", "all4", "AAA")

pancreas.list <- c[c("cite", "all4", "AAA")]
for (i in 1:length(pancreas.list)) {
pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE)
}

pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)
#options(future.globals.maxSize= 6091289600)
#pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = pancreas.features,
#verbose = FALSE) #future.globals.maxsize was to low. changed it to options(future.globals.maxSize= 1091289600)
#identify anchors

#alternative from tutorial (https://satijalab.org/seurat/articles/integration_introduction.html)
#memory.limit(9999999999)
features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)
pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = features)
pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT", anchor.features = pancreas.features, verbose = FALSE)
pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",
verbose = FALSE)

setwd("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper")

saveRDS(pancreas.integrated, file = "integrated_AAA_Cite_AMI.rds")

saveRDS(cd45, file = "integrated_AAA_Cite_CD45.rds")

seurat <- pancreas.integrated

#seurat <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/integrated_d5_cite.rds")

DefaultAssay(object = seurat) <- "integrated"
seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 3000)
seurat <- ScaleData(seurat, verbose = FALSE)
seurat <- RunPCA(seurat, npcs = 30, verbose = FALSE)
seurat <- FindNeighbors(seurat, dims = 1:30)
seurat <- FindClusters(seurat, resolution = 0.5)
seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:30)
DimPlot(seurat, reduction = "umap", split.by = "treatment") + NoLegend()

DimPlot(seurat, label = T, repel = T) + NoLegend()

DefaultAssay(object = seurat) <- "ADT"
adt_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.3)
write.csv(adt_marker_integrated, file = "adt_marker_all4_integrated.csv")

DefaultAssay(object = seurat) <- "RNA"
RNA_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.5)
write.csv(RNA_marker_integrated, file = "RNA_marker_all4_integrated.csv")

DimPlot(seurat, label = T, repel = T, split.by = "tissue") + NoLegend()

FeaturePlot(seurat, features = "Cd40", order = T, label = T)
FeaturePlot(seurat, features = "Ms.CD40", order = T, label = T)

#####
#leanup:
> seurat@meta.data[["sen_score1"]] <- NULL
> seurat@meta.data[["sen_score2"]] <- NULL
> seurat@meta.data[["sen_score3"]] <- NULL
> seurat@meta.data[["sen_score4"]] <- NULL
> seurat@meta.data[["sen_score5"]] <- NULL
> seurat@meta.data[["sen_score6"]] <- NULL
> seurat@meta.data[["sen_score7"]] <- NULL
> seurat@meta.data[["pANN_0.25_0.1_1211"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_1211"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_466"]] <- NULL
> seurat@assays[["prediction.score.celltype"]] <- NULL
> seurat@meta.data[["predicted.celltype"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_184"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_953"]] <- NULL
> seurat@meta.data[["integrated_snn_res.3"]] <- NULL
> seurat@meta.data[["RNA_snn_res.3"]] <- NULL
> seurat@meta.data[["SingleR"]] <- NULL
> seurat@meta.data[["SingleR_fine"]] <- NULL
> seurat@meta.data[["ImmGen"]] <- NULL
> seurat@meta.data[["ImmGen_fine"]] <- NULL
> seurat@meta.data[["percent.mt"]] <- NULL
> seurat@meta.data[["nCount_integrated"]] <- NULL
> seurat@meta.data[["nFeature_integrated"]] <- NULL
> seurat@meta.data[["S.Score"]] <- NULL
> seurat@meta.data[["G2M.Score"]] <- NULL
> seurat@meta.data[["Phase"]] <- NULL
> seurat@meta.data[["sen_score8"]] <- NULL
> seurat@meta.data[["sen_score9"]] <- NULL
> seurat@meta.data[["sen_score10"]] <- NULL
> seurat@meta.data[["sen_score11"]] <- NULL
> seurat@meta.data[["sen_score12"]] <- NULL
> seurat@meta.data[["sen_score13"]] <- NULL
> seurat@meta.data[["sen_score14"]] <- NULL
> seurat@meta.data[["sen_score15"]] <- NULL
> seurat@meta.data[["sen_score16"]] <- NULL
> seurat@meta.data[["sen_score17"]] <- NULL
> seurat@meta.data[["sen_score18"]] <- NULL
> seurat@meta.data[["sen_score19"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_184"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_953"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_466"]] <- NULL

Additional file 1: Fig. S1. Quality control measurements for larval Aedes VNC dataset. (A) Histogram showing the log10 distribution of cells expressing the indicated number of UMIs. Red line represents the nUMI cutoff, with cells below 1000 UMI excluded from further analysis. (B) Histogram showing the log10 distribution of cells with the indicated number of expressed genes (nGene). Red line represents the nGene cutoff; cells with fewer than 500 expressed genes were excluded from further analysis. (C) Histogram showing the distribution of cells with indicated proportion of mitochondrial transcripts. Red line represents the mitochondrial proportion cutoff (18%), above which cells were eliminated from further analysis. (D) Histogram showing the distribution of cells with the indicated proportion of transcripts from ribosomal genes. Cells with ribosomal gene proportions less than 5% or greater than 40% were eliminated from further analysis. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S2. Identification of putative glial cells in the larval Aedes VNC. (A) Cell atlas from initial clustering, which contains 25 distinct cell clusters. Putative glial cells, identified based on marker gene expression, are indicated. (B) Cells from this putative glial cluster were isolated and reclustered to reveal putative glial subtypes. Feature plots depict expression of glial marker genes including the pan-glial marker repo, cortex glia marker wrapper, surface glia marker gemini and four astrocyte glial markers wunen-2 (wun2), Excitatory amino acid transporter 1 (Eaat1), Gat and glutamine sythetase [57]. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S3. Distribution of neuronal marker gene expression in the Aedes larval VNC cell atlas. Feature plots depict expression of nSyb, Syt, and CadN which are present in all clusters. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S4. Neurotransmitter marker gene expression in the larval Aedes VNC. (A-D) Feature plots showing expression of neurotransmitter marker genes ChAT, VGAT, Vmat, and Dat. (E) Histogram showing number and percentage of cells expressing the indicated neurotransmitter marker genes. (F) Venn diagram showing the number of cells expressing the cholinergic marker genes VAChT and ChAT. Among the 329 ChAT+ VAChT- cells, 240 expressed an additional FAN marker gene (VGlut, VGAT and/or Gad1). (G) Maximum projections of confocal stacks depicting anti-ChAT immunostaining of the abdominal ventral ganglion, which was additionally labeled with the nucleic acid stain Hoechst. ChAT immunoreactivity is distributed throughout the neuropil but largely absent from the cortical cell layers. (H) Maximum projections of confocal stacks depicting anti-VGlut immunostaining of the abdominal ventral ganglion, which was additionally labeled with the nucleic acid stain Hoechst. Punctate VGlut immunoreactivity was present throughout the ganglion. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S5. Identifying subsets of monoaminergic neurons. Neurons that highly expressed monoaminergic marker genes were isolated from the VNC cell atlas and re-clustered to identify monoaminergic subsets. (A-D) Feature plots depict expression of the monoaminergic marker genes Tbh, SerT, Dat, and TH. Note that these genes are expressed in mutually exclusive sub-populations. (E) Seurat UMAP plot depicting monoaminergic neuron subtypes assigned on the basis of marker gene expression. (F-H) Feature plots depict expression of FAN markers VAChT (F), Gad1 (G), and VGlut (H) in monoaminergic neurons. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S6. Identification of transcriptional markers for serotonergic neurons. Neurons that expressed the serotonergic marker SerT were isolated and re-analyzed. Feature plots depict expression of SerT (A) and two transcription factor genes en (B) and Hox-C3a (C) whose expression is highly correlated with SerT (D-E). Expression correlation plots additionally depict Gad1 expression colored according to a lookup table. Note that cells with the highest levels of SerT, en, and Hox-C3a exhibit low expression of Gad1. (F) Maximum projections of confocal stacks depict representative images of the abdominal VNC labeled with antibodies to serotonin (5-HT) and en as well as the nucleic acid stain Hoechst. Cells co-expressing 5-HT and en are indicated with yellow arrowheads. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S7. Classification of dopaminergic neurons according to TH levels and GABA expression. (A) Histogram showing the distribution of dopaminergic cells with indicated intensities of anti-TH fluorescence (CTCF, corrected total cell fluorescence). Cells were classified according to breaks in the distribution: low TH cells had TH levels below 15,000 CTCF; high TH cells had TH levels greater than 50,000 CTCF; medium TH cells had intermediate TH levels (between 15,000 and 50,000 CTCF). (B) Histogram depicts the average number of low-, medium-, and high-expressing TH cells in each segment of the larval VNC. N = 6 animals, 251 neurons. (C-E) Relationship between TH and GABA expression. (C) Histogram showing the number of GABA-positive and GABA-negative low-, medium-, and high-expressing TH cells. Most TH+, GABA+ double-positive cells have low anti-TH levels. N = 3 animals, 129 neurons. (D) High TH-expressing cells (n = 60) were isolated from the global VNC dataset and reclustered. The feature plot depicts expression of the GABAergic neuron marker gene VGAT in dopaminergic neurons. (E) TH and Gad1 expression levels are inversely related in dopaminergic neurons. Scatter plot depicts expression of TH (x-axis), Gad1 (y-axis), and VGAT (shading) in 106 dopaminergic neurons. High-TH-expressing cells express low levels of Gad1 and VGAT1, and vice-versa. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S8. Expression patterns of neuropeptides and peptide processing enzymes. Dot plots depict expression levels of (A) neuropeptide genes and (B) neuropeptide processing enzyme genes in each cell cluster of the VNC cell atlas. Dot color indicates mean expression level across the cluster and dot diameter indicates the proportion of cells in the cluster with nonzero expression of the gene. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S9. Transcriptional markers of VNC cell clusters. Dot plots depict expression of transcription factors identified as marker genes for the indicated cell clusters, grouped according to transmitter identity: aminergic and peptidergic (A), cholinergic (B), GABAergic (C), and glutamatergic neurons (D). Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S10. Co-expression of neuropeptide genes in neurosecretory cluster 19. (A) Histogram showing the number and proportion of cluster 19 cells that express the following neuropeptide genes: DH31, Mip, burs, CCAP, pburs, and ASTCC. (B) Histogram showing the number and proportion of cluster 19 cells that co-express multiple neuropeptides in the same cell. Half of cluster 19 cells express 6 neuropeptide genes simultaneously. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S11. Expression pattern of the transcription factor gene ct. (A) Feature plot showing ct expression across the VNC cell atlas. (B) Maximum projections of confocal stacks depicting anti-Ct immunoreactivity in a representative segment of the abdominal VNC additionally labeled with the nuclear dye Hoechst. As with ct mRNA, Ct protein is broadly expressed at varying levels in the VNC. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S12. Expression patterns of neurotransmitter and neuropeptide receptor genes. Dot plots depict expression levels of (A) FAN receptor genes, (B) monoamine receptor genes, and (C) neuropeptide receptor genes in each cell cluster of the Aedes VNC cell atlas. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S13. Expression patterns of Hox transcription factors. (A) Schematic diagram indicating putative expression patterns of Hox genes mapped alongside a composite image of the larval VNC and segmental nerves visualized in a fillet preparation of a brp-T2A-QF2w, QUAS-mcd8GFP larva. (B) Maximum intensity projections depict representative results from antibody staining of thoracic (T2) and abdominal (A4) segments of the larval VNC with antibodies to Antp and Ubx/Abd-A (antibodies recognize both Ubx and Abd-A). (C) Feature plots depicting Hox gene expression across the larval VNC cell atlas. (D-E) Histograms depict (D) the overall number of cells and (E) the proportion of cells from each cluster which express the indicated Hox genes. Colors are used to indicate neuronal subtypes. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S14. Marker gene expression in neural progenitors and immature neurons. Dot plot depicts expression levels of pan-neuronal and FAN marker genes in cluster 10 sub-clusters. Putative neuroblasts (sub-cluster 4) exhibit limited expression of neuronal marker genes, sub-cluster 0 expresses neuronal markers but no FAN markers, and sub-clusters 1–3 expressed neuronal markers and an individual FAN marker. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S15. Global pseudotime analysis of VNC development. (A) Pseudo-time trajectory analysis of the entire cell atlas, anchored at cluster 10. Cells are colored according to their pseudo-time score along the trajectory, with darker colors marking more immature neurons and lighter colors indicating less immature neurons. (B) UMAP plot highlighting cluster 6 and cluster 10, both of which are comprised principally of immature neurons. (C) Box plots showing pseudotime scores for each cluster. Clusters are grouped according to their principle FAN and ordered according to pseudotime scores. Genotype: brp-T2A-QF2w / +; QUAS-mcd8GFP / +. Fig. S16. Identification of mitotic cells in the larval VNC. (A) Maximum intensity projection of confocal stack depicting anti-PH3 immunoreactivity in thoracic ventral ganglion additionally labeled with anti-Lamin antibodies

##### CD40 inhibiton in AMI on d5, seq on d7 and d14

# Load necessary libraries for data manipulation, analysis, and visualization

library(dplyr)

library(Seurat)

library(patchwork)

library(plyr)

# Set the working directory to the folder containing the data

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/938-2_cellranger_count/outs")

# Read the M0 dataset from the 10X Genomics format

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

RNA <- pbmc.data$`Gene Expression`

ADT <- pbmc.data$`Antibody Capture`

HST <- pbmc.data$`Multiplexing Capture`

# Load the Matrix package

library(Matrix)

# Hashtag 1, 2 and 3 are marking the mouse replicates per condition

# Subset the rows based on row names

subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")

animals_data <- HST[subsetted_rows, , drop = FALSE]

# Hashtag 4, 5, 6, 7 are representing DMSO d7, TS d7, DMSO d14 and TS d14

subsetted_rows <- c(""TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")

treatment_data <- HST[subsetted_rows, , drop = FALSE]

#Create a Seurat obeject and more assays to combine later

RNA <- CreateSeuratObject(counts = RNA)

ADT <- CreateAssayObject(counts = ADT)

Mice <- CreateAssayObject(counts = animals_data)

Treatment <- CreateAssayObject(counts = treatment_data)

seurat <- RNA

#Add the Assays

seurat[["ADT"]] <- ADT

seurat[["HST_Mice"]] <- Mice

seurat[["HST_Treatment"]] <- Treatment

#Check for AK Names

rownames(seurat[["ADT"]])

#Cluster cells on the basis of their scRNA-seq profiles

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

FeaturePlot(seurat, features = "Col1a1", order = T)

# Normalize ADT data,

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

#Demultiplex cells based on Mouse_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.60)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Mice_classification.global)

DimPlot(seurat, group.by = "HST_Mice_classification")

#Demultiplex cells based on Treatment_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.60)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Treatment_classification.global)

DimPlot(seurat, group.by = "HST_Treatment_classification")

Idents(seurat) <- seurat$HST_Treatment_classification

pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)

Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification

pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

#Redo the clssification to remove the doublettes

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)

table(pbmc.singlet$HST_Treatment_classification.global)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)

table(pbmc.singlet$HST_Mice_classification.global)

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")

seurat <- pbmc.singlet

seurat$mice <- seurat$HST_Mice_maxID

seurat$treatment <- seurat$HST_Treatment_maxID

library(plyr)

seurat$treatment <- revalue(seurat$treatment, c(

"TotalSeq-B0304" = "DMSO_d7",

"TotalSeq-B0305" = "TS_d7",

"TotalSeq-B0306" = "DMSO_d14",

"TotalSeq-B0307" = "TS_d14"

))

library(plyr)

seurat$mice <- revalue(seurat$mice, c(

"TotalSeq-B0301" = "1",

"TotalSeq-B0302" = "2",

"TotalSeq-B0303" = "3"

))

#Cluster cells on the basis of their scRNA-seq profiles without doublettes

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/Analyse")

saveRDS(seurat, file= "TSd5.v0.1.RDS")

TLDRSeurat object of the 16 NPM1-mutated AML samples (n = 83,162 cells).AML samplesAll sixteen peripheral blood and bone marrow samples were obtained from patients with AML at diagnosis (n=15) or relapse after chemotherapy (n=1) with written informed consent according to the Declaration of Helsinki. Mononuclear cells were isolated by Ficoll-Isopaque density gradient centrifugation and cryopreserved in the Leiden University Medical Center (LUMC) Biobank for Hematological Diseases after approval by the LUMC Institutional Review Board (protocol no. B18.047).Upstream processing pipelineCellRanger v7.0.0 was run on all samples with the human reference genome hg38. For all QC Seurat v4 was used15. Our QC pipeline had three steps per sample: 1) soft filtering, 2) low quality cluster removal, and 3) doublet detection. In soft filtering, Seurat objects were created with cells expressing at least 200 genes and with the genes expressed at least in 3 cells. Then, standard Seurat command list with default parameters was run to detect low quality clusters. Clusters with >15% mitochondrial and 15% mitochondrial mRNA. We used standard Seurat commands to scale and normalize the data on integrated features. First 30 principal components were used to create UMAP plots. We used clustree to determine optimal cluster number, based on FindClusters with resolutions sweeping from 0 to 1.2. We chose res=0.5, as clusters became stable. Next, we merged two clusters (CC5 and CC12) into one GMP-like cluster as one of these clusters (CC12) had high expression of HSP-genes yet still retained its cell-type specific properties.Note: The file was processed with Seurat v4 but the object is updated for v5. Uploaded as .qs file format for faster reading. To read the file: qs:qread("path/to/data.qs")This data is available for research use only; and cannot be used for commercial purposes.For further queries please refer to our paper:

The purpose of these studies is to investigate how Sphingosine-1-phosphate (S1P) signaling regulates glial phenotype, dedifferentiation of Müller glia (MG), reprogramming into proliferating MG-derived progenitor cells (MGPCs), and neuronal differentiation of the progeny of MGPCs in the chick retina. We found that S1P-related genes are highly expressed by retinal neurons and glia, and levels of expression were dynamically regulated following retinal damage. Drug treatments that activate S1P receptor 1 (S1PR1) or increase levels of S1P suppressed the formation of MGPCs. Conversely, treatments that inhibit S1PR1 or decrease levels of S1P stimulated the formation of MGPCs. Inhibition of S1P receptors or S1P synthesis significantly enhanced the neuronal differentiation of the progeny of MGPCs. We report that S1P-related gene expression in MG is modulated by microglia and inhibition of S1P receptors or S1P synthesis partially rescues the loss of MGPC formation in damaged retinas missing micro..., We analyzed scRNA-seq libraries that were generated and characterized previously (Campbell et al., 2021b; Campbell et al., 2022; El-Hodiri et al., 2022; El-Hodiri et al., 2023, 2021; Hoang et al., 2020; Li et al., 2023; Lyu et al., 2023). Dissociated cells were loaded onto the 10X Chromium Cell Controller with Chromium 3’ V2, V3 or Next GEM reagents. Using Seurat toolkits (Powers and Satija, 2015; Satija et al., 2015), Uniform Manifold Approximation and Projection (UMAP) for dimensional reduction plots were generated from 9 separate cDNA libraries, including 2 replicates of control undamaged retinas, and retinas at different times after NMDA-treatment. Seurat was used to construct gene lists for differentially expressed genes (DEGs), violin/scatter plots, and dot plots. Significance of difference in violin/scatter plots was determined using a Wilcoxon Rank Sum test with Bonferroni correction. Genes that were used to identify different types of retinal cells included the following: (1) M..., , # Sphingosine-1-phosphate signaling regulates the ability of Müller glia to become neurogenic, proliferating progenitor-like cells

https://doi.org/10.5061/dryad.tdz08kq8t

General Information

Dataset Overview

A detailed description of the general framework and specific methodology can be found in the relevant publication (https://doi.org/10.7554/eLife.102151.4).Â

For each dataset, barcode, feature, and matrix file from CellRanger output are provided. These files serve as inputs for preparing the Seurat objects used in this study. Barcode files contain a list of cell barcodes. Feature files contain gene names from the reference used for CellRanger and include 3 columns: ENSEMBL number, gene name, and the type of assay run ("GENE EXPRESSION"). Matrix files contain the sparse matrix containing UMI counts for each library.

Dissociated cells were loaded onto the 10X Chromium Cell Controller ...,