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Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Enhancer mapping has been greatly facilitated by various genomic marks associated with it. However, little is available in our toolbox to link enhancers with their target promoters, hampering mechanistic understanding of enhancer-promoter (EP) interaction. We develop and characterize multiple genomic features for distinguishing true EP pairs from noninteracting pairs. We integrate these features into a probabilistic predictor for EP interactions. Multiple validation experiments demonstrate a significant improvement over state-of-the-art approaches. Systematic analyses of EP interactions across 12 cell types reveal several global features of EP interactions: (i) a larger fraction of EP interactions are cell type specific than enhancers; (ii) promoters controlled by multiple enhancers have higher tissue specificity, but the regulating enhancers are less conserved; (iii) cohesin plays a role in mediating tissue-specific EP interactions via chromatin looping in a CTCF-independent manner. Our approach presents a systematic and effective strategy to decipher the mechanisms underlying EP communication.T ranscriptional enhancers represent the primary basis for differential gene expression. These elements regulate cell type specificity, development, and metazoan evolution, with many human diseases resulting from altered enhancer action (1, 2).A key gap in our knowledge is an understanding of how enhancers select specific promoters for activation. Linkage of enhancers and target promoters is challenged by enhancer properties. First, increasing evidence suggests that enhancers are not located adjacent to their target promoters. Instead, they are positioned tens of kilobases away and contact their targets via long-range interactions (3-6). Second, enhancers are position independent, i.e., they may be located either upstream or downstream of the regulated promoter.Experimental approaches to identifying enhancer targets have largely relied on chromosome conformation capture (3C) (7) and its variants such as circularized chromosome conformation capture (4C) and genome-wide chromosome conformation capture (Hi-C) (8), all of which determine the relative frequency of direct physical contact between linearly separated DNA sequences. Unlike 3C and 4C, Hi-C is a truly genome-wide technology, but its current resolution (1 Mbp) in general is not high enough to distinguish individual enhancer-promoter (EP) interactions (9). Newer methods such as ChIP-loop (10) and chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) (11) combine the principles of 3C and ChIP to identify chromatin interactions mediated by protein factors. However, the assays are technically challenging and currently have a high false-negative rate (5, 12). Therefore, computational work, if successful, can complement experimental protocols and allow prioritization of future experiments much more effectively.The most common computational approach is assigning the nearest promoter of an enhancer as its target. Improvements to this basic approach have be...

The sensitivity of chromatin immunoprecipitation (ChIP) assays poses a major obstacle for epigenomic studies of low-abundance cells. Here we present a microfluidics-based ChIP-Seq protocol using as few as 100 cells via drastically improved collection of high-quality ChIP-enriched DNA. Using this technology, we uncovered many novel enhancers and super enhancers in hematopoietic stem and progenitor cells from mouse fetal liver, suggesting that enhancer activity is highly dynamic during early hematopoiesis.