CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data.

Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.

NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology.

Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.

Clusterin immunoreactivity as a predictive factor for progression of non-muscle-invasive bladder carcinoma.

INTRODUCTION: There is a need for prognostic markers which can predict the subset of patients who will not respond sufficiently to conservative management in non-muscle-invasive bladder carcinoma. We analyzed the association of clusterin (CLU) with clinicopathological factors. MATERIALS AND METHODS: Immunohistochemical CLU expression was investigated in paraffin-embedded archival tissues of initial transurethral resection specimens of 46 patients with non-muscle-invasive bladder carcinoma. The result was expressed as the proportion of the number of CLU-containing tumor cells to the total number of tumor cells detected in each slide and 'percent CLU expression' was calculated for each patient. RESULTS: Of the 46 cases (35 male, 11 female), 18 were ≥ 65 years of age. CLU expression was significantly higher in male and elderly patients. Following the initial transurethral resection, 39 patients showed tumor recurrence, and progression was seen in 25 patients, of whom 17 progressed to muscle invasion during follow-up. Although there was no significant correlation between CLU expression and recurrence, significant correlation with overall progression and progression to muscle-invasive disease was observed in this cohort of patients (p = 0.001 and p = 0.014, respectively). Among the patients with progression to muscle invasion, 13 underwent radical cystectomy with pT2 tumor in 5 patients in the final pathology of surgical specimens and pT3 and higher in the remainder. CONCLUSIONS: CLU immunoreactivity showed correlation with age, gender and progression, mainly progression to muscle invasion. Thus, CLU can be used as a molecular marker to predict the potential of progression to muscle-invasive disease in a particular tumor which in turn may prove useful in the decision-making process for early cystectomy without losing time with conservative management.

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data.

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

Sexual-dimorphism in human immune system aging.

Differences in immune function and responses contribute to health- and life-span disparities between sexes. However, the role of sex in immune system aging is not well understood. Here, we characterize peripheral blood mononuclear cells from 172 healthy adults 22-93 years of age using ATAC-seq, RNA-seq, and flow cytometry. These data reveal a shared epigenomic signature of aging including declining naïve T cell and increasing monocyte and cytotoxic cell functions. These changes are greater in magnitude in men and accompanied by a male-specific decline in B-cell specific loci. Age-related epigenomic changes first spike around late-thirties with similar timing and magnitude between sexes, whereas the second spike is earlier and stronger in men. Unexpectedly, genomic differences between sexes increase after age 65, with men having higher innate and pro-inflammatory activity and lower adaptive activity. Impact of age and sex on immune phenotypes can be visualized at https://immune-aging.jax.org to provide insights into future studies.