SUMMARY Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

SUMMARY Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remains poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel candidate drivers with biological, and possibly therapeutic, importance in cancer.

We present a method that harnesses massively parallel DNA synthesis and sequencing for the highthroughput functional analysis of regulatory sequences at single-nucleotide resolution. As a proof of concept, we quantitatively assayed the effects of all possible single-nucleotide mutations for three bacteriophage promoters and three mammalian core promoters in a single experiment per promoter. The method may also serve as a rapid screening tool for regulatory element engineering in synthetic biology.A broad range of methods exist for annotating functional regulatory elements in genomes. These include comparative and ab initio prediction algorithms 1-3 and high-throughput assays such as ChIP-Seq 4 and CAGE 5,6 . Despite much progress, the architectures of the vast majority of regulatory elements have yet to be systematically and quantitatively dissected at high resolution. Effective methods for this include classical saturation mutagenesis 7 and combinatorial promoter shuffling 8,9 , but these have been applied only at low throughput. Furthermore, the effects of promoter modification are measured using techniques that are not always sufficiently sensitive to detect subtle changes in transcription.Here we present a high-throughput method to systematically analyze the effect in a single experiment of mutations at every position in a core promoter (Fig. 1a). Mutant promoters are synthesized in parallel as DNA oligonucleotides on a programmable microarray and released into solution 10 , resulting in a complex library. Each oligonucleotide in the library is designed to include a unique barcode sequence downstream of the promoter's transcription start site (TSS). The oligos are transcribed in vitro, and the resulting transcripts are sequenced. The relative abundance of each programmed barcode provides a digital readout of the transcriptional efficiency of its cis-linked mutant promoter.As a proof of concept, this method was applied to three well-characterized bacteriophage promoters: T3 (class 3, phi13), T7 (class 3, phi10) and SP6 (SP6p32). We focused on a 35-nt region, spanning 23-nt upstream and 12-nt downstream of each promoter's TSS (Fig. 1b). At each position, we mutated the native nucleotide to every other nucleotide or introduced a singlenucleotide deletion. We also included several double mutation promoters, allowing us to compare the single mutants to their combination. To guard against the potential influence of the barcode itself on transcriptional activity, we represented each mutant variant of each native Correspondence should be addressed to J.S. (shendure@u.washington.edu) or R.P.P. (rpatward@u.washington.edu). Tables 1 and 2). NIH Public AccessThe promoter library was transcribed in vitro with one of three RNA polymerases (T7, T3 or SP6). The resulting RNA pools were reverse transcribed, PCR amplified and sequenced on an Illumina GAII system. Reads were then mapped back to the 20-nt barcodes that we had programmed in cis with each synthetic promoter. To control for potentially non-uniform represe...

Hepatocyte-specific gene expression requires the interaction of many proteins with multiple binding sites in the regulatory regions. HNF-3 is a site found to be important in the maximal hepatocyte-specific expression of several genes. We find that liver nuclear extracts contain three major binding activities for this site, which we call HNF-3A, HNF-3B, and HNF-3C. Purification from rat liver nuclear extracts of HNF-3A and HNF-3C reveals that each activity corresponds to a distinct polypeptide, as determined by SDS-PAGE. Peptide sequence derived from the most abundant species, HNF-3A, was used for synthesizing probes with which to isolate a cDNA clone of this protein. The encoded protein contains 466 amino acids (48.7 kD) and has binding properties identical to those of the purified protein. A 160-amino-acid region that does not resemble the binding domain of any known transcription factor is essential for DNA binding. The mRNA for HNF-3A is present in the rat liver but not in brain, kidney, intestine, or spleen, and the basis for this difference is cell-specific regulation of HNF-3A gene transcription.

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, like mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4+ T-lymphocytes, we find that although these two cell subtypes had similarly-wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated MAP kinase (ERK2), which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells compared to antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single cell data, we can algorithmically derive response functions underlying molecular circuits and drive the understanding of how cells process signals.