Journal: STAR Protocols

Article Title: Co-staining human PBMCs with fluorescent antibodies and antibody-oligonucleotide conjugates for cell sorting prior to single-cell CITE-Seq

doi: 10.1016/j.xpro.2021.100893

Figure Lengend Snippet:

Article Snippet: SeqGeq™ v1.6.0 software (analysis and visualization of single-cell CITE-Seq data) , BD Biosciences , https://www.flowjo.com/solutions/seqgeq.

Techniques: Recombinant, Staining, Cell Culture, Blocking Assay, Amplification, Software, Sequencing, Flow Cytometry, Transferring

Journal: bioRxiv

Article Title: Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

doi: 10.1101/2022.06.08.495292

Figure Lengend Snippet: (A) Schematic of GoT-Splice workflow. The combination of GoT with CITE-seq and long-read full-length cDNA using Oxford Nanopore Technologies (ONT) enables the simultaneous profiling of protein and gene expression, somatic mutation status, and alternative splicing at single-cell resolution. (B) Summary of patient metadata and GoT data (after quality control) for MDS and CH samples with SF3B1 mutations. (C) Uniform manifold approximation and projection (UMAP) of CD34+ cells ( n = 15,436 cells) from myelodysplastic syndrome patient samples with SF3B1 K700E mutations ( n = 3 individuals), overlaid with cluster cell-type assignments. HSPC, hematopoietic stem progenitor cells; IMP, immature myeloid progenitors; MkP, megakaryocytic progenitors; MEP, megakaryocytic-erythroid progenitors; EP, erythroid progenitors; NP, neutrophil progenitors; E/B/M, eosinophil/basophil/mast progenitor cells; T/B cells; Mono, monocyte; DC, dendritic cells; Pre-B, precursors B cells; Mono DC, monocyte/dendritic cell progenitors. (D) Density plot of SF3B1 mut vs. SF3B1 wt cells. Genotyping information (MDS01-03) was obtained for 12,494 cells (80.9 % of all cells). (E) Normalized frequency of SF3B1 K700E MUT cells in progenitor subsets with at least 300 genotyped cells. Bars show aggregate analysis of samples MDS01-03 with mean +/-s.e.m. of 100 downsampling iterations to 1 genotyping UMI per cell. Only cell types with >300 cells were used in the analysis. P -value from likelihood ratio test of linear mixed model with or without mutation status. (F) Differential ADT marker expression between SF3B1 mut and SF3B1 wt cells. Red: higher expression in SF3B1 mut cells; blue: higher expression in SF3B1 wt cells. Size of the dot corresponds to the average expression of ADT marker across cells in a given cell-type. P -values determined through permutation testing. (G) Mutant cell fraction and ADT expression levels of CD36 and CD71 as a function of pseudotime along the megakaryocyte-erythroid differentiation trajectory for SF3B1 mut and SF3B1 wt cells in MDS01-03. Shading denotes 95% confidence interval. Histogram shows cell density of clusters included in the analysis, ordered by pseudotime. P -values were calculated by Wilcoxon rank sum test by comparing mutant cell fraction between pseudotime trajectory quartiles. (H) Differential gene expression between SF3B1 mut and SF3B1 wt EP cells in MDS samples. Genes with an absolute log 2 (fold change) > 0.1 and P -value < 0.05 were defined as differentially expressed (DE). DE genes belonging to cell cycle (red) and translation (blue) pathways (Reactome) are highlighted (BH-FDR < 0.2). (I) Expression (mean +/-s.e.m.) of TP53 pathway related genes (Reactome) between SF3B1 mut and SF3B1 wt cells in progenitor cells from MDS01-03 samples. Red: module score in SF3B1 mut cells; blue: module score in SF3B1 wt cells. P -values from likelihood ratio test of linear mixed model with or without mutation status. (J) Same as (I) for expression of cell cycle related genes (Reactome) between SF3B1 mut and SF3B1 wt cells in progenitor cells from MDS01-03 samples.

Article Snippet: GoT-Splice with CITE-seq integrates Genotyping of Transcriptomes (GoT) with both long-read single-cell transcriptome profiling (with Oxford Nanopore Technologies [ONT]) and proteogenomics (with CITE-seq).

Techniques: Expressing, Mutagenesis, Alternative Splicing, Marker

Journal: bioRxiv

Article Title: Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

doi: 10.1101/2022.06.08.495292

Figure Lengend Snippet: (A) SF3B1 K700E (7) and K666N (1) mutant cell fractions determined by GoT in single cells versus SF3B1 K700E and K666N mutation variant allele frequencies (VAF) determined in bulk sequencing of matched unsorted bone marrow mononuclear cells (MDS) or matched unsorted stem cell product (CH). (B) Fraction of cells in MDS01-03 by number of SF3B1 UMIs in standard 10x Genomics data without genotyping information (left), SF3B1 UMIs with K700E locus coverage in standard 10x data (middle), and SF3B1 UMIs with K700E locus coverage in GoT amplicon library (right). (C) UMAP of progenitor cells from MDS01-03 overlaid with genotyping data. WT, cells with genotype data without SF3B1 mutation; MUT, cells with genotype data with SF3B1 mutation; NA, unassignable cells with no genotype data. (D) UMAP of progenitor cells from MDS04-06 overlaid with genotyping data. WT, cells with genotype data without SF3B1 mutation; MUT, cells with genotype data with SF3B1 mutation; NA, unassignable cells with no genotype data. (E) Normalized ratio of SF3B1 mut cells in progenitor subsets with at least 300 genotyped cells. Bars show aggregate analysis of samples MDS01-03 with mean +/-s.e.m. of 100 downsampling iterations to 1 genotyping UMI per cell. Points represent the mean of n = 100 downsampling iteration per sample.

Article Snippet: GoT-Splice with CITE-seq integrates Genotyping of Transcriptomes (GoT) with both long-read single-cell transcriptome profiling (with Oxford Nanopore Technologies [ONT]) and proteogenomics (with CITE-seq).

Techniques: Mutagenesis, Variant Assay, Sequencing, Amplification

Journal: bioRxiv

Article Title: Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

doi: 10.1101/2022.06.08.495292

Figure Lengend Snippet: (A) UMAP of CD34+ cells ( n = 9,007 cells) from clonal hematopoiesis (CH) samples, one with SF3B1 K700E mutation and one with SF3B1 K666N mutation ( n = 2 individuals), overlaid with cluster cell-type assignments. HSPC, hematopoietic stem progenitor cells; IMP, immature myeloid progenitors; MEP, megakaryocytic-erythroid progenitors; EP, erythroid progenitors; MkP, megakaryocytic progenitors; NP, neutrophil progenitors; E/B/M, eosinophil/basophil/mast progenitor cells; Pre-B, precursors B cells. (B) UMAP of CD34+ cells from CH samples overlaid with genotyping data. WT, cells with genotype data without SF3B1 mutation; MUT, cells with genotype data with SF3B1 mutation; NA, unassignable cells with no genotype data. (C) UMAP of CD34+ cells from CH samples overlaid with pseudotemporal ordering. Inset: Pseudotime in SF3B1 mut vs. SF3B1 wt cells in the aggregate of CH01-02. P -value for comparison of means from Wilcoxon rank sum test. (D) Normalized ratio of mutated cells along pseudotime quartiles. Bars show aggregate analysis of samples CH01-CH02 with mean +/-s.e.m. of 100 downsampling iterations to 1 genotyping UMI per cell. Only cell types with >300 cells were used in the analysis. P -value from likelihood ratio test of linear mixed model with or without mutation status. Bottom : Fraction of cell types within each pseudotime quartile. (E) Differential gene expression between SF3B1 mut and SF3B1 wt HSPC cells in CH samples. Genes with an absolute log 2 (fold change) > 0.1 and P -value < 0.05 were defined as differentially expressed (DE). DE genes belonging to the translation pathway (red, Reactome) are highlighted (BH-FDR < 0.2). (F) Gene Set Enrichment Analysis of DE genes in SF3B1 mut HSPC cells across CH samples. Gene sets that overlap with SF3B1 mut EP cells in MDS highlighted (red). (G) Expression (mean +/-s.e.m.) of mRNA translation-related genes (Reactome) between SF3B1 mut and SF3B1 wt cells in progenitor cells from CH01-02 samples. P -values from likelihood ratio test of linear mixed model with or without mutation status.

Article Snippet: GoT-Splice with CITE-seq integrates Genotyping of Transcriptomes (GoT) with both long-read single-cell transcriptome profiling (with Oxford Nanopore Technologies [ONT]) and proteogenomics (with CITE-seq).

Techniques: Mutagenesis, Comparison, Expressing

Journal: bioRxiv

Article Title: Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

doi: 10.1101/2022.06.08.495292

Figure Lengend Snippet: (A) Fraction of cells in CH01-02 by number of SF3B1 UMIs in standard 10x Genomics data without genotyping information (left), SF3B1 UMIs with K666N (CH01) or K700E (CH02) locus coverage in standard 10x data (middle), and SF3B1 UMIs with K666N (CH01) or K700E (CH02) locus coverage in GoT amplicon library (right). (B) Normalized ratio of SF3B1 mut cells in HSPC and EP cells for CH01 and CH02. Bars show the mean of n = 100 downsampling iterations to 1 genotyping UMI per cell. (C) Per sample heatmap of relative expression of genes ordered by chromosome/chromosomal position following copy number variation analysis using the InferCNV package (see Methods). Cells (y-axis) are stratified by SF3B1 genotype status. (D) Pseudotime in SF3B1 mut vs. SF3B1 wt cells per CH sample. P -value for comparison of means from Wilcoxon rank sum test.

Article Snippet: GoT-Splice with CITE-seq integrates Genotyping of Transcriptomes (GoT) with both long-read single-cell transcriptome profiling (with Oxford Nanopore Technologies [ONT]) and proteogenomics (with CITE-seq).

Techniques: Amplification, Expressing, Comparison

Journal: Frontiers in Molecular Biosciences

Article Title: The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

doi: 10.3389/fmolb.2022.962644

Figure Lengend Snippet: Overview of recently published deep learning-based methods to integrate single-cell multi-omics data. 1 Only for mapping single-omics to multi-omics; 2 Only when converting peaks to activity scores.

Article Snippet: For the CITE-seq dataset, Cobolt and SCALEX perform best, with Cobolt surpassing SCALEX for increasing cell numbers. scMM consistently has the lowest scores on the CITE-seq data.

Techniques: Activity Assay, Flow Cytometry

Journal: Frontiers in Molecular Biosciences

Article Title: The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

doi: 10.3389/fmolb.2022.962644

Figure Lengend Snippet: Biological preservation metrics. NMI, cell type ASW, and trajectory conservation score indicate the biological preservation quality achieved by joint embeddings from various models. On the 10x Multiome data, the performance of Portal, scMVP, Cobolt, scMM, DAVAE, MultiVI is shown (top), whereas the performance on CITE-seq data is shown for Cobolt, TotalVI, SCALEX, and scMM (bottom). Median scores across all iterations are shown inside the tiles.

Article Snippet: For the CITE-seq dataset, Cobolt and SCALEX perform best, with Cobolt surpassing SCALEX for increasing cell numbers. scMM consistently has the lowest scores on the CITE-seq data.

Techniques: Preserving

Journal: Frontiers in Molecular Biosciences

Article Title: The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

doi: 10.3389/fmolb.2022.962644

Figure Lengend Snippet: Technical effect removal metrics. Site ASW, batch ASW, and graph connectivity score indicate the quality of technical effects removal achieved by joint embeddings from various models. On the 10x Multiome data, the performance of Portal, scMVP, scMM, Cobolt, DAVAE, and MultiVI is shown (top), whereas the performance on CITE-seq data is shown for the SCALEX, Cobolt, TotalVI, and scMM (bottom). Median scores across all iterations are shown inside the tiles.

Article Snippet: For the CITE-seq dataset, Cobolt and SCALEX perform best, with Cobolt surpassing SCALEX for increasing cell numbers. scMM consistently has the lowest scores on the CITE-seq data.

Techniques:

Journal: Frontiers in Molecular Biosciences

Article Title: The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

doi: 10.3389/fmolb.2022.962644

Figure Lengend Snippet: UMAP of the 10-dimensional latent space of Cobolt, scMM, TotalVI, and SCALEX based on 500 (top) and 10,000 (bottom) cells of one exemplary subsample from the CITE-seq dataset each. The color coding corresponds to manually annotated cell types as provided by . The following cell types are not present in the 500 cell sample: CD4 + T CD314+ CD45RA+, CD8 + T naive CD127+ CD26 − CD101-, cDC1, dnT, Plasma cell IGKC-, Plasma cell IGKC+, Plasmablast IGKC-, T prog cycling.

Article Snippet: For the CITE-seq dataset, Cobolt and SCALEX perform best, with Cobolt surpassing SCALEX for increasing cell numbers. scMM consistently has the lowest scores on the CITE-seq data.

Techniques: