reagent kit bundle  (Thermo Fisher)

Journal: eLife

Article Title: Ex vivo expansion potential of murine hematopoietic stem cells is a rare property only partially predicted by phenotype

doi: 10.7554/eLife.91826

Figure Lengend Snippet: ( A ) Frequency and fold change of phenotypic candidate HSCs (cHSCs) (EPCR high SLAM LSKs) in ex vivo cultures after 14 or 21 days of culture (n=12 per group). Data points depict values from individual cultures initiated from 50 cHSCs. Error bars denote SEM. The asterisks indicate significant differences. ****, p<0.0001. ( B ) UMAP (based on SAILERX dimensionality reduction) of single-cell multiome profiling of cells expanded ex vivo for 21 days. Cell-type annotations were derived using marker gene signatures and distal motif identities. ( C ) Trajectory analysis of lineage differentiation for cells expanded ex vivo (left), with the top 3 scoring TF motifs of each cluster (right). ( D ) Expression of HSC signature on whole culture. ( E ) Expression of HSC signature of EPCR + cells sorted from ex vivo cultures. ( F ) Cell cycle phase classification of EPCR + cells sorted from ex vivo cultures. Figure 2—source data 1. Raw data for : Frequency and fold change of phenotypic candidate hematopoietic stem cells (cHSCs) expanded ex vivo.

Article Snippet: Multiome sequencing experiments were performed at the Center for Translational Genomics (Lund University) using the Chromium Next GEM Single Cell Multiome ATAC+Gene Expression Reagent Bundle kit according to the manufacturer’s instructions (10x Genomics).

Techniques: Ex Vivo, Derivative Assay, Marker, Expressing

Journal: Genome Research

Article Title: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

doi: 10.1101/gr.277960.123

Figure Lengend Snippet: Overview of scTIE, a unified framework for the integration of temporal data and the inference of context-specific GRNs that predict cell fates. The input of scTIE consists of the gene expression matrix of scRNA-seq and peak matrix of scATAC-seq from single-cell multiome data over a time course. scTIE consists of two main steps. ( A ) In the first step, each cell, represented by a pair of gene and peak vectors, is projected into a common embedding space by separate encoders and decoders. The two modalities and time points are aligned by appropriate loss functions, whereas the transition probability matrix between cells from consecutive time points is iteratively estimated. ( B ) In the second step, users have the ability to select specific subgroups of cells whose transitions are of interest, finetune the previously trained neural network, identify features that are predictive of transition probabilities, and construct the corresponding GRNs.

Article Snippet: The single-cell multiome library was generated using a chromium next GEM single-cell multiome ATAC + gene expression reagent bundle kit (10x Genomics, PN-1000283).

Techniques: Gene Expression, Construct

Journal: Genome Research

Article Title: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

doi: 10.1101/gr.277960.123

Figure Lengend Snippet: Performance benchmarking for integrating temporal multimodal data. ( A ) Joint visualization using UMAP of the synthetic data set with batch effect in RNA and noise in ATAC, colored by cell type annotations ( top ), sampling days ( middle ), and synthetic batch information ( bottom ). Each dot represents a cell in the embedding space. ( B ) Bar plots showing the evaluation metrics of different data integration methods, including ARI values for clustering with annotations ( left ), 1 − average purity scores of sampling days with the number of neighbors equal to 50 ( middle ), and 1 − average purity scores of the synthetic batch with the number of neighbors equal to 50 ( right ). Higher values indicate better agreement with annotations and mixing of batches/days. ( C ) Radar plot summarizing the three evaluation metrics shown in B , in which each line represents the performance of one method, and each axis represents an evaluation metric, starting from the minimum value of all methods. It is noted that scAI was not included in this benchmarking owing to its long computational time (>2 d).

Article Snippet: The single-cell multiome library was generated using a chromium next GEM single-cell multiome ATAC + gene expression reagent bundle kit (10x Genomics, PN-1000283).

Techniques: Sampling

Journal: Genome Research

Article Title: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

doi: 10.1101/gr.277960.123

Figure Lengend Snippet: Integration and cell type identification of the mESC data set by scTIE. ( A ) Joint visualization of the mESC data set using UMAP, colored by sampling day and cell type annotations. Each dot represents a cell in the embedding space. ( B ) Cell type compositions per time point. ( C ) Dot plots of mean expression of RNA data. Rows represent cell types, and columns indicate each gene. The color scale represents the expression level, and the size indicates proportion of positively expressed cells. The five most significantly expressed genes for each cluster are included. ( D ) Heatmap of the TF motif enrichment ( Z -scores) of ATAC data. Rows represent cell types, and columns indicate TFs. The five most significantly enriched TFs for each cluster are included. ( E ) Scatter plots of the mean RNA expression levels by clusters ( x -axis) and the average TF motif enrichment scores of ATAC ( y -axis) for the selected TFs. The dots are colored by the cell type annotations, with the color legend consistent with that in A .

Article Snippet: The single-cell multiome library was generated using a chromium next GEM single-cell multiome ATAC + gene expression reagent bundle kit (10x Genomics, PN-1000283).

Techniques: Sampling, Expressing, RNA Expression

Journal: Genome Research

Article Title: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

doi: 10.1101/gr.277960.123

Figure Lengend Snippet: Biological signals in the mESC data set captured by each embedding dimension of scTIE. ( A ) Enrichment scores of the gradient ranking in each embedding dimension using the RNA ( top ) and ATAC ( bottom ) marker list for each cell type. ( B ) Gene Ontology enrichment of selected pathways on the gradient ranking of a subset of embedding dimensions. ( C ) Gradient rankings for RNA ( top ) and ATAC ( bottom ) of embedding dimension 39, in which genes/peaks are ranked based on the gradient values. The labeled points are genes in the selected gene set (activin receptor signaling pathway).

Article Snippet: The single-cell multiome library was generated using a chromium next GEM single-cell multiome ATAC + gene expression reagent bundle kit (10x Genomics, PN-1000283).

Techniques: Marker, Labeling

Journal: Genome Research

Article Title: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

doi: 10.1101/gr.277960.123

Figure Lengend Snippet: Autoencoder architecture for RNA ( left ) and ATAC ( right )

Article Snippet: The single-cell multiome library was generated using a chromium next GEM single-cell multiome ATAC + gene expression reagent bundle kit (10x Genomics, PN-1000283).

Techniques:

Journal: Developmental cell

Article Title: Single-cell epigenome analysis identifies molecular events controlling direct conversion of human fibroblasts to pancreatic ductal-like cells.

doi: 10.1016/j.devcel.2023.08.023

Figure Lengend Snippet: Figure 4. FI-snMultiome method for dissecting the role of defined TFs in reprogramming at single-cell resolution (A) Schematic presentation of the lentiviral expression construct for barcoded TFs and the strategy for capturing the barcodes during 103 Multiome snRNA-seq workflow. UMI, unique molecular identifier. (B) Reprogrammed cells transduced with six barcoded TFs individually or as a pool were harvested at different time points and multiplexed for the analysis of transcriptomic (snRNA-seq) and epigenetic (snATAC-seq) changes from the same cell. Custom TF barcode library was generated by an additional PCR after the pre-amplification step during the 103 Multiome workflow (see STAR Methods), enabling correlation of the TF barcodes with the 103 cell barcodes during downstream analysis. (C) Uniform manifold approximation and projection (UMAP) plots of all cells from different time points (indicated with colors) based on gene expression (left) and chromatin accessibility (middle) separately and their integrated profiles using weighted nearest neighbor (WNN) analysis (right). HFFs transduced with green fluorescent protein (GFP) reporter gene were used as a control. For all FI-snMultiome-seq analyses, 5,399 individual cells from 1 week, 6,921 from 2 weeks, and 10,276 from 48 h and HFFs were used. (D) UMAPs showing the transcript levels of endogenous TFs (left), exogenous TFs detected from their barcodes (middle), and motif accessibility analyzed from the snATAC-seq data (right).

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Goat Anti-FOXA2 R&D AF2400; RRID: AB_2294104 Goat Anti-SOX17 R&D AF1924; RRID: AB_355060 Mouse Anti-PDX-1 (B-11) Santa Cruz Biotechnology sc-390792; RRID: AB_2938928 Mouse Anti-HNF-6 (G-10) Santa Cruz Biotechnology sc-376167, RRID: AB_10989089 Rabbit Anti-SOX9 Millipore AB5535, RRID: AB_2239761 Rabbit Anti-HNF1B Sigma-Aldrich HPA002083; RRID: AB_1080232 Rabbit Anti-Osteopontin Proteintech 22952-1-AP, RRID: AB_2783651 Donkey Anti-Goat (Alexa Fluor 488) secondary antibody Abcam ab150129, RRID: AB_2687506 Goat Anti-Mouse (Alexa Fluor 488) secondary antibody Thermo Fisher Scientific A-11029, RRID: AB_2534088 Goat Anti-Rabbit (Alexa Fluor 594) secondary antibody Thermo Fisher Scientific A-11012, RRID: AB_2534079 Goat Anti-Rabbit (Alexa Fluor 488) secondary antibody Thermo Fisher Scientific A-11034, RRID: AB_2576217 PE Anti-Human CD24 BioLegend 311105, RRID: AB_314854 APC Anti-Human CD133/2 Miltenyi Biotec 130-113-746, RRID: AB_2726285 Rabbit Anti-H3K27ac Diagenode C15410196, RRID: AB_2637079 Normal rabbit IgG Santa Cruz Biotechnology sc-2027, RRID: AB_737197 Bacterial and virus strains One Shot Stbl3 Chemically Competent E. coli Thermo Fisher Scientific C737303 One Shot ccdB Survival 2 T1R Competent Cells Thermo Fisher Scientific A10460 Chemicals, peptides, and recombinant proteins Ascorbic Acid Sigma-Aldrich A1300000 Retinoic Acid Sigma-Aldrich R2625 PD0325901 Sigma-Aldrich PZ0162 CHIR99021 StemMACS 130-106-539 LDN193189 StemMACS 130-103-925 Activin A Peprotech AF-120-14E FGF7 Peprotech AF-100-19 Critical commercial assays Carbonic Anhydrase Activity Assay Kit Biovision K472 Trypsin Activity Colorimetric Assay Kit Sigma-Aldrich MAK290 Amylase Activity Assay kit Sigma-Aldrich MAK009 KAPA mRNA HyperPrep Kit Roche KR1352 Illumina Tagment DNA Enzyme and Buffer Small Kit Illumina 20034197 Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10xGenomics PN-1000285 Deposited data Raw data This paper GEO: GSE216859 Bulk RNA-seq data of human acinar cells Perkins et al.19 GEO: GSE179248 Bulk RNA-seq data of human ductal cells Ayars et al.20 GEO: GSE96784 Single-cell RNA-seq data of human pancreatic cells Segerstolpe et al.49 ArrayExpress: E-MTAB-5060 Human reference genome NCBI build 38, GRCh38 Genome Reference Consortium https://www.ncbi.nlm.nih.gov/ assembly/GCF_000001405 ENCODE blacklisted genomic regions for hg38 ENCODE ENCODE: ENCFF356LFX Experimental models: Cell lines HFF ATCC CRL-2429 (Continued on next page) e1 Developmental Cell 58, 1701–1715.e1–e8, September 25, 2023

Techniques: Expressing, Construct, Transduction, Generated, Gene Expression, Control