Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Granja, Jeffrey M.; Klemm, Sandy; McGinnis, Lisa M.; Kathiria, Arwa S.; Mezger, Anja; Corces, M. Ryan; Parks, Benjamin; Gars, Eric J.; Liedtke, Michaela; Zheng, Grace; Chang, Howard Y.; Majeti, Ravindra; Greenleaf, William J.

doi:10.1038/s41587-019-0332-7

Cited by 344 publications

(526 citation statements)

References 52 publications

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“…2c and Supplementary Table 4). Unbiased iterative clustering 12, 18 of these single cells identified 24 distinct clusters (Figure 2a) which were assigned to known brain cell types based on gene activity scores (see Methods) compiled from chromatin accessibility signal in the vicinity of key lineage-defining genes 18, 19 (Figure 2b and Supplementary Fig. 2c).…”

Section: Resultsmentioning

confidence: 99%

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Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Corces

Shcherbina

Kundu

et al. 2020

Preprint

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42Genome-wide association studies (GWAS) have identified thousands of variants associated with 43 disease phenotypes. However, the majority of these variants do not alter coding sequences, making 44 it difficult to assign their function. To this end, we present a multi-omic epigenetic atlas of the 45 adult human brain through profiling of the chromatin accessibility landscapes and three-46 dimensional chromatin interactions of seven brain regions across a cohort of 39 cognitively healthy 47 individuals. Single-cell chromatin accessibility profiling of 70,631 cells from six of these brain 48 regions identifies 24 distinct cell clusters and 359,022 cell type-specific regulatory elements, 49 capturing the regulatory diversity of the adult brain. We develop a machine learning classifier to 50 integrate this multi-omic framework and predict dozens of functional single nucleotide 51 polymorphisms (SNPs), nominating gene and cellular targets for previously orphaned GWAS loci. 52These predictions both inform well-studied disease-relevant genes, such as BIN1 in microglia for 53 Alzheimer's disease (AD) and reveal novel gene-disease associations, such as STAB1 in microglia 54 and MAL in oligodendrocytes for Parkinson's disease (PD). Moreover, we dissect the complex 55 inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying ectopic enhancer-gene 56 contacts in neurons that increase MAPT expression and may mediate this disease association. This 57 work greatly expands our understanding of inherited variation in AD and PD and provides a 58 roadmap for the epigenomic dissection of noncoding regulatory variation in disease. 59 60 61 62Alzheimer's disease (AD) and Parkinson's disease (PD) affect ~50 and ~10 million individuals 63 world-wide, as two of the most common neurodegenerative disorders. Several large consortia have 64 assembled genome-wide association studies (GWAS) that associate genetic variants with clinical 65 diagnoses of probable AD dementia 1-4 or probable PD 5-7 , or with their characteristic pathologic 66 features. These efforts have led to the identification of dozens of potential risk loci for these 67 prevalent neurodegenerative diseases. One goal of these studies was to build more precise 68 molecular biomarkers of AD or PD, efforts that are beginning to yield encouraging results with 69 polygenic risk scores 8 . The other major goal was to gain deeper insight into the molecular 70 pathogenesis of disease and thereby inform novel therapeutic targets. Some of the risk loci contain 71 coding variants and so have credibility as putative disease mediators. However, most risk loci are 72 in noncoding regions and so it remains unclear if the nominated (often nearest) gene is the 73 functional disease-relevant gene, or if some other gene is involved 9 . Furthermore, even if the 74 nominated gene is a true positive, the noncoding risk locus might regulate additional genes. These 75 challenges remain a fundamental gap in interpreting the etiology of neurodegenerative diseases 76 and d...

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Section: Resultsmentioning

confidence: 99%

“…To cluster our scATAC-seq data, we first identified a robust set of peak regions followed by iterative LSI clustering 12, 18 . Briefly, we created 1-kb windows tiled across the genome and determined whether each cell was accessible within each window (binary).…”

Section: Methodsmentioning

confidence: 99%

Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Corces

Shcherbina

Kundu

et al. 2020

Preprint

Self Cite

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“…In this section, we will demonstrate LIGER's ability to jointly define cell types by leveraging multiple single-cell modalities. We integrate published human bone marrow mononuclear cell (BMMC) data 16 profiled by single-cell RNA-seq and single-nucleus ATAC-seq to enable cell type definitions that incorporate both gene expression and chromatin accessibility data. Such joint analysis allows not only the taxonomic categorization of cell types, but also a deeper understanding of their underlying regulatory networks.…”

Section: Joint Definition Of Cell Types From Single-cell Gene Expressmentioning

confidence: 99%

Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER

Liu

Gao

Sodicoff

et al. 2020

Preprint

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High-throughput single-cell sequencing technologies hold tremendous potential for defining cell types in an unbiased fashion using gene expression and epigenomic state. A key challenge in realizing this potential is integrating single-cell datasets from multiple protocols, biological contexts, and data modalities into a joint definition of cellular identity. We previously developed an approach called Linked Inference of Genomic Experimental Relationships (LIGER) that uses integrative nonnegative matrix factorization to address this challenge. Here, we provide a step-by-step protocol for using LIGER to jointly define cell types from multiple single-cell datasets. The main steps of the protocol include data preprocessing and normalization, joint factorization, quantile normalization and joint clustering, and visualization. We describe how to jointly define cell types from single-cell RNA-seq and single-nucleus ATAC-seq data, but similar steps apply across a wide range of other settings and data types, including cross-species analysis, single-nucleus DNA methylation, and spatial transcriptomics. Our protocol contains examples of expected results, describes common pitfalls, and relies only on our freely available, open-source R implementation of LIGER. We also provide Rmarkdown tutorials showing the outputs from each individual code segment. The analysis process can be performed in 1 -4 h depending on dataset size and assumes no specialized bioinformatics training. ifnb_liger <-normalize(ifnb_liger) ifnb_liger <-selectGenes(ifnb_liger) ifnb_liger <-scaleNotCenter(ifnb_liger) ?TROUBLESHOOTING Stage II: Joint Matrix Factorization (3-10 minutes) 4.We are now able to run integrative non-negative matrix factorization on the normalized and scaled datasets. The key parameter for this analysis is k , the number of matrix factors (analogous to the bc <-genes.bc[, 7 ] bc_split <-strsplit(bc, ";" ) bc_split_vec <-unlist(bc_split) bc_unique <-unique(bc_split_vec) bc_counts <-table(bc_split_vec) bc_filt <-names(bc_counts)[bc_counts > 1500 ] barcodes <-bc_filt

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“…Then for each organ, cell types were annotated based on the cell co-embedding of the transcriptome landscape and Cicero gene activity scores (Pliner et al, 2018) using Seurat (Stuart et al, 2019), and 177 cell clusters were obtained (see Methods Figures S6A and S6B). Interestingly, the genomic accessibility in TF motifs was strongly correlated with TF RNA expression levels ( Figure 4B), which suggested that the chromatin accessibility could reflect TF activity veritably (Granja et al, 2019).…”

Section: The Construction Of Single-cell Open Chromatin Landscapementioning

confidence: 92%

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

Tian

Zhou

et al. 2020

Preprint

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Highlights• Genomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetus • Determining correlated gene modules (CGMs) in different cell types by MALBAC-DT • Measuring chromatin open regions in single cells with high detectability by METATAC • Integrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM Summary By circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq(METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent.We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.

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Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Cited by 344 publications

References 52 publications

Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

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