Project description:Background: A better understanding of the pancreatic adenocarcinoma (PDAC) immune microenvironment is critical to improving outcomes. Myeloid cells are of particular importance for PDAC progression. Circulating monocytes are recruited and hijacked by the tumor to become immune suppressive tumor-associated macrophages. Meanwhile, tissue resident macrophages are innately anti-inflammatory and pro-fibrotic. Open chromatin and transcription factor activity provide source and function data for myeloid cells. Thus, we explore single nuclear assay for transposase accessible chromatin (ATAC) sequencing (snATAC-Seq), a method to analyze open gene promoters and transcription factor binding, as an important means for discerning the myeloid composition in human PDAC tumors. Methods: Frozen pancreatic tissues (benign or PDAC) were digested into single nuclei suspensions. snATAC-Seq and sn-Multiome (ATAC + RNA) libraries were prepared using 10X Chromium technology and sequenced by an Illumina sequencer. Signac was used for preliminary analysis, clustering, and differentially accessible chromatin region identification. snMultiome data was used to validate cell annotations for the snATAC-Seq data. Results: Unique populations comprised the benign and tumor samples. Myeloid cell transcription factor activities were higher in tumor samples than benign pancreatic samples, enabling us to further stratify myeloid populations in PDAC tumors. GO enrichment analysis showed myeloid cells can activate immune response in tumors. Subcluster analysis revealed eight distinct myeloid populations. GO enrichment demonstrated unique functions of different myeloid populations, including IL-1b signaling (recruited monocytes) and intracellular protein transport (dendritic cells). Conclusions: These data suggest snATAC-Seq as a method for retrospective analysis of frozen human pancreatic tissues to identify myeloid populations. Understanding the source specific potential function of myeloid cells is important for patient prognosis in PDAC.

Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.

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Comparative analysis of human brain tissue preprocessing methods for single-nucleus chromatin accessibility profiling

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