?Fig.1a-represented1a-represented Mono/Mac cluster. transcription elements (B), pattern recognition receptors (C), cell adhesion/migration molecules (D), and chemokine receptors (E). F, The Kaplan-Meier overall survival curves of TCGA AML patients grouped by specific DC subset (pDC, CLEC7A+ DC, and CD1C+ DC) gene sets. + represents censored observations, and value was calculated by multivariate Cox regression. 40364_2021_265_MOESM3_ESM.tif (6.3M) GUID:?63D8619F-5036-46E5-B41D-1A5A3CA6F8FA Additional file 4: Supplementary Figure 3. A, UMAP plot of Monocyte/Macrophages from Fig. ?Fig.1a-represented1a-represented Mono/Mac cluster. These mature myeloid cells can be divided into 10 subsets before filtering possible cell-cell complexes. B, Expression levels of CD14 and CD3D across Mono/Mac population illustrated in UMAP plots. C, The Kaplan-Meier overall survival curves of TCGA AML patients grouped by specific subset gene sets. + represents censored observations, and value was calculated by multivariate Cox regression. 40364_2021_265_MOESM4_ESM.tif (7.0M) GUID:?D04BF41F-CC41-4E5D-8C0A-11B77F745302 Additional file 5: Supplementary Figure 4. A, dynamic changes of proportion of distinct cell-types in total T/NK cells before and after treatment, and healthy donor-derived BM cells, as control, are represented at the end of plots. B, The Kaplan-Meier overall survival curves of Betulinic acid TCGA AML patients grouped by specific NK/NKT-like gene set and IFN-CD4+ gene set. + represents censored observations, and value was calculated by multivariate Cox regression. 40364_2021_265_MOESM5_ESM.tif (11M) GUID:?CA8B0552-161C-416D-BAAB-BCD3C57F38AC Additional file 6: Supplementary Betulinic acid Figure 5. A, Violin plot showing the expression levels of in 4 clusters (CD69highGZMA-CD4+ T, CD69highGZMA+CD4+ T, CD4+ Cytotoxic T, CD69lowLTBhighCD4+ T) from Fig. ?Fig.5a-represented5a-represented cells. B, The state-space of Na?ve CD4+ T cluster, TH17-like cluster, and Treg cluster. Each dot corresponded to one single cell, colored according to its state (total 6 states). C, Expression maps showing log-normalized expression of typical markers (and his colleagues [32]. The GSM numbers of all these samples with other detailed information (days from diagnosis, gender, age, mutations, and so on) are listed in Supplementary Table?1. Quality control and data processing Single-cell datasets of AML patients and healthy donors were integrated using merge function in version 3.2.2 of Seurat R package [33]. We filtered cells that have unique feature counts over 3000, less than 200, and??10% mitochondrial Rabbit Polyclonal to UNG counts. The Betulinic acid merged dataset was normalized using Seurat NormalizedData function with a global-scaling normalization method LogNormalize, and multiplied this by a scale factor (10,000 by default). And then scaled by performing Seurat ScaleData function with regression of the variation of nCount_RNA and percent.mt. Performing Seurat JackStrawPlot function and ElbowPlot function helped to select suitable dimensionality. Dimension reduction analysis was performed by Seurat RunPCA function, and non-linear dimensional reduction was performed Betulinic acid by Seurat RunUMAP function. Reconstructing cell development trajectories To explore the developmental progression of na?ve CD4+ T cells to TH17-like cells and/or Treg cells, we used Monocle package (version 2.14.0) for reconstructing their development trajectories [34]. We extracted the dataset of na?ve CD4+ cluster, TH17-like cluster, and Treg cluster, and then selected the cluster feature genes for the trajectory reconstruction. Survival analysis The TCGA AML data (file TCGA-LAML.htseq_fpkm.tsv, file TCGA-LAML.survival.tsv, and file gencode.v22.annotation.gene.probeMap) were download from UCSC Xena (http://xena.ucsc.edu/) [35] and used to assess the prognostic effect of single Betulinic acid functional genes, preference gene sets, and gene sets from cluster biomarkers. Cluster biomarkers were got through performing Seurat FindAllMarkers function and reporting only the positive ones. We used package survival and survminer packages to get the survival curve. Results A scRNA-seq census of AML BM immune cells pre- and post-treatment We hypothesized the immune phenotypes and status were remodeled by uncontrollable AML blasts, it might be identifiable in data generated from recent efforts to distinguish AML hierarchies [32]. and his colleagues showed an atlas of AML cell states by scRNA-seq, and found monocyte-like AML cells suppressed T cell activity by expressing immunomodulatory genes [32]. To characterize the dynamic changes of mature hematopoietic cell lineages states at more refined levels, we first downloaded and.