Identifying single-cell molecular programs by stochastic profiling

Surviving apoptosis: life-death signaling in single cells

Tissue development and homeostasis are regulated by opposing pro-survival and pro-death signals. An interesting feature of the Tumor Necrosis Factor (TNF) family of ligands is that they simultaneously activate opposing signals within a single cell via the same ligand-receptor complex. The magnitude of pro-death events such as caspase activation and pro-survival events such as Nuclear Factor (NF)-κB activation vary not only from one cell type to the next but also among individual cells of the same type due to intrinsic and extrinsic noise. The molecules involved in these pro-survival and/or pro-death pathways, and the different phenotypes that result from their activities, have been recently reviewed. Here we focus on the impact of cell-to-cell variability in the strength of these opposing signals on shaping cell fate decisions.

An analysis of critical factors for quantitative immunoblotting

Immunoblotting (also known as Western blotting) combined with digital image analysis can be a reliable method for analyzing the abundance of proteins and protein modifications, but not every immunoblot-analysis combination produces an accurate result. I illustrate how sample preparation, protocol implementation, detection scheme, and normalization approach profoundly affect the quantitative performance of immunoblotting. This study implemented diagnostic experiments that assess an immunoblot-analysis workflow for accuracy and precision. The results showed that ignoring such diagnostics can lead to pseudoquantitative immunoblot data that markedly overestimate or underestimate true differences in protein abundance.

Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability

Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

On comparing heterogeneity across biomarkers

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

Single-cell RNA-seq: advances and future challenges

Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward 'systems pharmacology', we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction.

A time- and matrix-dependent TGFBR3-JUND-KRT5 regulatory circuit in single breast epithelial cells and basal-like premalignancies

Basal-like breast carcinoma is characterized by poor prognosis and high intratumour heterogeneity. In an immortalized basal-like breast epithelial cell line, we identified two anticorrelated gene-expression programs that arise among single extracellular matrix (ECM)-attached cells during organotypic three-dimensional culture. The first contains multiple TGF-β-related genes including TGFBR3, whereas the second contains JUND and the basal-like marker KRT5. TGFBR3 and JUND interconnect through four negative-feedback loops to form a circuit that exhibits spontaneous damped oscillations in three-dimensional culture. The TGFBR3-JUND circuit is conserved in some premalignant lesions that heterogeneously express KRT5. The circuit depends on ECM engagement, as detachment causes a rewiring that is triggered by RPS6 dephosphorylation and maintained by juxtacrine tenascin C, which is critical for intraductal colonization of basal-like breast cancer cells in vivo. Intratumour heterogeneity need not stem from partial differentiation and could instead reflect dynamic toggling of cells between expression states that are not cell autonomous.

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles

Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.

SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism

To search for evolutionary forces that might act upon transcript length, we use the singular value decomposition (SVD) to identify the length distribution functions of sets and subsets of human and yeast transcripts from profiles of mRNA abundance levels across gel electrophoresis migration distances that were previously measured by DNA microarrays. We show that the SVD identifies the transcript length distribution functions as “asymmetric generalized coherent states” from the DNA microarray data and with no a-priori assumptions. Comparing subsets of human and yeast transcripts of the same gene ontology annotations, we find that in both disparate eukaryotes, transcripts involved in protein synthesis or mitochondrial metabolism are significantly shorter than typical, and in particular, significantly shorter than those involved in glucose metabolism. Comparing the subsets of human transcripts that are overexpressed in glioblastoma multiforme (GBM) or normal brain tissue samples from The Cancer Genome Atlas, we find that GBM maintains normal brain overexpression of significantly short transcripts, enriched in transcripts that are involved in protein synthesis or mitochondrial metabolism, but suppresses normal overexpression of significantly longer transcripts, enriched in transcripts that are involved in glucose metabolism and brain activity. These global relations among transcript length, cellular metabolism and tumor development suggest a previously unrecognized physical mode for tumor and normal cells to differentially regulate metabolism in a transcript length-dependent manner. The identified distribution functions support a previous hypothesis from mathematical modeling of evolutionary forces that act upon transcript length in the manner of the restoring force of the harmonic oscillator.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Non-biased and efficient global amplification of a single-cell cDNA library

Analysis of single-cell gene expression promises a more precise understanding of molecular mechanisms of a living system. Most techniques only allow studies of the expressions for limited numbers of gene species. When amplification of cDNA was carried out for analysing more genes, amplification biases were frequently reported. A non-biased and efficient global-amplification method, which uses a single-cell cDNA library immobilized on beads, was developed for analysing entire gene expressions for single cells. Every step in this analysis from reverse transcription to cDNA amplification was optimized. By removing degrading excess primers, the bias due to the digestion of cDNA was prevented. Since the residual reagents, which affect the efficiency of each subsequent reaction, could be removed by washing beads, the conditions for uniform and maximized amplification of cDNAs were achieved. The differences in the amplification rates for randomly selected eight genes were within 1.5-folds, which could be negligible for most of the applications of single-cell analysis. The global amplification gives a large amount of amplified cDNA (>100 μg) from a single cell (2-pg mRNA), and that amount is enough for downstream analysis. The proposed global-amplification method was used to analyse transcript ratios of multiple cDNA targets (from several copies to several thousand copies) quantitatively.

Simultaneous profiling of 194 distinct receptor transcripts in human cells

Many signal transduction cascades are initiated by transmembrane receptors with the presence or absence and abundance of receptors dictating cellular responsiveness. We provide a validated array of quantitative reverse transcription polymerase chain reaction (qRT-PCR) reagents for high-throughput profiling of the presence and relative abundance of transcripts for 194 transmembrane receptors in the human genome. We found that the qRT-PCR array had greater sensitivity and specificity for the detected receptor transcript profiles compared to conventional oligonucleotide microarrays or exon microarrays. The qRT-PCR array also distinguished functional receptor presence versus absence more accurately than deep sequencing of adenylated RNA species by RNA sequencing (RNA-seq). By applying qRT-PCR-based receptor transcript profiling to 40 human cell lines representing four main tissues (pancreas, skin, breast, and colon), we identified clusters of cell lines with enhanced signaling capabilities and revealed a role for receptor silencing in defining tissue lineage. Ectopic expression of the interleukin-10 (IL-10) receptor-encoding gene IL10RA in melanoma cells engaged an IL-10 autocrine loop not otherwise present in this cell type, which altered signaling, gene expression, and cellular responses to proinflammatory stimuli. Our array provides a rapid, inexpensive, and convenient means for assigning a receptor signature to any human cell or tissue type.

The applications of single-cell genomics

We all start out as a single totipotent cell that is programmed to produce a multicellular organism. How do individual cells make those complex developmental switches? How do single cells within a tissue or organ differ, how do they coordinate their actions or go astray in a disease process? These are long-standing and fundamental questions in biology that are now becoming tractable because of advances in microfluidics, DNA amplification and DNA sequencing. Methods for studying single-cell transcriptomes (or at least the polyadenylated mRNA fraction of it) are by far the furthest ahead and reveal remarkable heterogeneity between morphologically identical cells. The analysis of genomic DNA variation is not far behind. The other 'omics' of single cells pose greater technological obstacles, but they are progressing and promise to yield highly integrated large data sets in the near future.

Identifying network motifs that buffer front-to-back signaling in polarized neutrophils

Neutrophil polarity relies on local, mutual inhibition to segregate incompatible signaling circuits to the leading and trailing edges. Mutual inhibition alone should lead to cells having strong fronts and weak backs or vice versa. However, analysis of cell-to-cell variation in human neutrophils revealed that back polarity remains consistent despite changes in front strength. How is this buffering achieved? Pharmacological perturbations and mathematical modeling revealed a functional role for microtubules in buffering back polarity by mediating positive, long-range crosstalk from front to back; loss of microtubules inhibits buffering and results in anticorrelation between front and back signaling. Furthermore, a systematic, computational search of network topologies found that a long-range, positive front-to-back link is necessary for back buffering. Our studies suggest a design principle that can be employed by polarity networks: short-range mutual inhibition establishes distinct signaling regions, after which directed long-range activation insulates one region from variations in the other.

Embracing noise to improve cross-batch prediction accuracy

One important application of microarray in clinical settings is for constructing a diagnosis or prognosis model. Batch effects are a well-known obstacle in this type of applications. Recently, a prominent study was published on how batch effects removal techniques could potentially improve microarray prediction performance. However, the results were not very encouraging, as prediction performance did not always improve. In fact, in up to 20% of the cases, prediction accuracy was reduced. Furthermore, it was stated in the paper that the techniques studied require sufficiently large sample sizes in both batches (train and test) to be effective, which is not a realistic situation especially in clinical settings. In this paper, we propose a different approach, which is able to overcome limitations faced by conventional methods. Our approach uses ranking value of microarray data and a bagging ensemble classifier with sequential hypothesis testing to dynamically determine the number of classifiers required in the ensemble. Using similar datasets to those in the original study, we showed that in only one case (<2%) is our performance reduced (by more than -0.05 AUC) and, in >60% of cases, it is improved (by more than 0.05 AUC). In addition, our approach works even on much smaller training data sets and is independent of the sample size of the test data, making it feasible to be applied on clinical studies.

Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

Cellular heterogeneity and molecular evolution in cancer

Intratumor heterogeneity represents a major obstacle to effective cancer treatment and personalized medicine. However, investigators are now elucidating intratumor heterogeneity at the single-cell level due to improvements in technologies. Better understanding of the composition of tumors, and monitoring changes in cell populations during disease progression and treatment, will improve cancer diagnosis and therapeutic design. Measurements of intratumor heterogeneity may also be used as biomarkers to predict the risk of progression and therapeutic resistance. We summarize important considerations related to intratumor heterogeneity during tumor evolution. We also discuss experimental approaches that are commonly used to infer intratumor heterogeneity and describe how these methodologies can be translated into clinical practice.

Measurement and modeling of signaling at the single-cell level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.

Expression of VEGF receptors on endothelial cells in mouse skeletal muscle

VEGFR surface localization plays a critical role in converting extracellular VEGF signaling towards angiogenic outcomes, and the quantitative characterization of these parameters is critical for advancing computational models; however the levels of these receptors on blood vessels is currently unknown. Therefore our aim is to quantitatively determine the VEGFR localization on endothelial cells from mouse hindlimb skeletal muscles. We contextualize this VEGFR quantification through comparison to VEGFR-levels on cells in vitro. Using quantitative fluorescence we measure and compare the levels of VEGFR1 and VEGFR2 on endothelial cells isolated from C57BL/6 and BALB/c gastrocnemius and tibialis anterior hindlimb muscles. Fluorescence measurements are calibrated using beads with known numbers of phycoerythrin molecules. The data show a 2-fold higher VEGFR1 surface localization relative to VEGFR2 with 2,000-3,700 VEGFR1/endothelial cell and 1,300-2,000 VEGFR2/endothelial cell. We determine that endothelial cells from the highly glycolytic muscle, tibialis anterior, contain 30% higher number of VEGFR1 surface receptors than gastrocnemius; BALB/c mice display ~17% higher number of VEGFR1 than C57BL/6. When we compare these results to mouse fibroblasts in vitro, we observe high levels of VEGFR1 (35,800/cell) and very low levels of VEGFR2 (700/cell), while in human endothelial cells in vitro, we observe that the balance of VEGFRs is inverted, with higher levels VEGFR2 (5,800/cell) and lower levels of VEGFR1 (1,800/cell). Our studies also reveal significant cell-to-cell heterogeneity in receptor expression, and the quantification of these dissimilarities ex vivo for the first time provides insight into the balance of anti-angiogenic or modulatory (VEGFR1) and pro-angiogenic (VEGFR2) signaling.

Teasing apart translational and transcriptional components of stochastic variations in eukaryotic gene expression

The intrinsic stochasticity of gene expression leads to cell-to-cell variations, noise, in protein abundance. Several processes, including transcription, translation, and degradation of mRNA and proteins, can contribute to these variations. Recent single cell analyses of gene expression in yeast have uncovered a general trend where expression noise scales with protein abundance. This trend is consistent with a stochastic model of gene expression where mRNA copy number follows the random birth and death process. However, some deviations from this basic trend have also been observed, prompting questions about the contribution of gene-specific features to such deviations. For example, recent studies have pointed to the TATA box as a sequence feature that can influence expression noise by facilitating expression bursts. Transcription-originated noise can be potentially further amplified in translation. Therefore, we asked the question of to what extent sequence features known or postulated to accompany translation efficiency can also be associated with increase in noise strength and, on average, how such increase compares to the amplification associated with the TATA box. Untangling different components of expression noise is highly nontrivial, as they may be gene or gene-module specific. In particular, focusing on codon usage as one of the sequence features associated with efficient translation, we found that ribosomal genes display a different relationship between expression noise and codon usage as compared to other genes. Within nonribosomal genes we found that sequence high codon usage is correlated with increased noise relative to the average noise of proteins with the same abundance. Interestingly, by projecting the data on a theoretical model of gene expression, we found that the amplification of noise strength associated with codon usage is comparable to that of the TATA box, suggesting that the effect of translation on noise in eukaryotic gene expression might be more prominent than previously appreciated.

The stochastic nature of gene expression leads to cell-to-cell differences in protein level referred to as noise. Expression noise can be disadvantageous, by affecting the precision of biological functions, but it may also be advantageous by enabling heterogeneous stress-response programs to environmental changes. Therefore various genes and gene groups might display various levels of expression noise. Importantly, gene expression is a multi-step process and the stochasticity of its individual steps, including transcription and translation, contributes to the resulting variability. Recent single cell analyses of gene expression in yeast have confirmed the theoretically predicted general trend where expression noise scales with protein abundance. However, some deviations from this basic trend have also been observed, prompting questions about the contribution of gene-specific features to such deviations. Accounting for noise heterogeneity in different gene groups, we revealed a clear relationship between noise and translation-related genomic features, specifically codon usage and 5′ UTR secondary structure. Our results suggest that the effect of translation on these deviations might be more prominent than previously appreciated, and provide important clues towards understanding expression stochasticity in yeast.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes

Transitions between the two phases of the cell growth cycle can account for the environmental stress response, the growth-rate response, and the cross-protection between slow growth and various types of stress factors. It is suggested that this mechanism is conserved across budding and fission yeast and normal human cells.

The respiratory metabolic cycle in budding yeast (Saccharomyces cerevisiae) consists of two phases that are most simply defined phenomenologically: low oxygen consumption (LOC) and high oxygen consumption (HOC). Each phase is associated with the periodic expression of thousands of genes, producing oscillating patterns of gene expression found in synchronized cultures and in single cells of slowly growing unsynchronized cultures. Systematic variation in the durations of the HOC and LOC phases can account quantitatively for well-studied transcriptional responses to growth rate differences. Here we show that a similar mechanism—transitions from the HOC phase to the LOC phase—can account for much of the common environmental stress response (ESR) and for the cross-protection by a preliminary heat stress (or slow growth rate) to subsequent lethal heat stress. Similar to the budding yeast metabolic cycle, we suggest that a metabolic cycle, coupled in a similar way to the ESR, in the distantly related fission yeast, Schizosaccharomyces pombe, and in humans can explain gene expression and respiratory patterns observed in these eukaryotes. Although metabolic cycling is associated with the G0/G1 phase of the cell division cycle of slowly growing budding yeast, transcriptional cycling was detected in the G2 phase of the division cycle in fission yeast, consistent with the idea that respiratory metabolic cycling occurs during the phases of the cell division cycle associated with mass accumulation in these divergent eukaryotes.

Akt and ERK control the proliferative response of mammary epithelial cells to the growth factors IGF-1 and EGF through the cell cycle inhibitor p57Kip2

Epithelial cells respond to growth factors including epidermal growth factor (EGF), insulin-like growth factor 1 (IGF-1), and insulin. Using high-content immunofluorescence microscopy, we quantitated differences in signaling networks downstream of EGF, which stimulated proliferation of mammary epithelial cells, and insulin or IGF-1, which enhanced the proliferative response to EGF but did not stimulate proliferation independently. We found that the abundance of the cyclin-dependent kinase inhibitors p21Cip1 and p57Kip2 increased in response to IGF-1 or insulin but decreased in response to EGF. Depletion of p57Kip2, but not p21Cip1, rendered IGF-1 or insulin sufficient to induce cellular proliferation in the absence of EGF. Signaling through the PI3K (phosphatidylinositol 3-kinase)-Akt-mTOR (mammalian target of rapamycin) pathway was necessary and sufficient for the increase in p57Kip2, whereas MEK [mitogen-activated or extracellular signal-regulated protein kinase (ERK) kinase]-ERK activity suppressed this increase, forming a regulatory circuit that limited proliferation in response to unaccompanied Akt activity. Knockdown of p57Kip2 enhanced the proliferative phenotype induced by tumor-associated PI3K mutant variants and released mammary epithelial acini from growth arrest during morphogenesis in three-dimensional culture. These results provide a potential explanation for the context-dependent proliferative activities of insulin and IGF-1 and for the finding that the CDKN1C locus encoding p57Kip2 is silenced in many breast cancers, which frequently show hyperactivation of the PI3K pathway. The status of p57Kip2 may thus be an important factor to assess when considering targeted therapy against the ERK or PI3K pathways.

Normal morphogenesis of epithelial tissues and progression of epithelial tumors

Epithelial cells organize into various tissue architectures that largely maintain their structure throughout the life of an organism. For decades, the morphogenesis of epithelial tissues has fascinated scientists at the interface of cell, developmental, and molecular biology. Systems biology offers ways to combine knowledge from these disciplines by building integrative models that are quantitative and predictive. Can such models be useful for gaining a deeper understanding of epithelial morphogenesis? Here, we take inventory of some recurring themes in epithelial morphogenesis that systems approaches could strive to capture. Predictive understanding of morphogenesis at the systems level would prove especially valuable for diseases such as cancer, where epithelial tissue architecture is profoundly disrupted.

RUNX1 and its understudied role in breast cancer

The transcription factor Runt-related transcription factor 1 (RUNX1) is critical for the earliest steps of hematopoiesis. RUNX1 was originally identified as a gene fusion in acute myeloid leukemia (AML) and thus has garnered heavy attention as a tumor suppressor in hematopoietic malignancies. However, RUNX1 is also strongly expressed in breast epithelia and may be misregulated during tumorigenesis. Here, I discuss our recent work implicating RUNX1 in proliferation control during breast epithelial-acinar morphogenesis. My goal is to place these findings in the context of a handful of other reports, which together argue that RUNX1 could act as a tumor suppressor gene in breast cancer. Testing this hypothesis requires focused in vivo studies, because the major commercial platform for global mRNA expression profiling does not reliably reflect RUNX1 levels. Our in vitro results indicate that hyperproliferation in RUNX1-deficient breast epithelia relies on another family of transcription factors, the Forkhead box O (FOXO) proteins. FOXOs could, therefore, represent a synthetic-lethal target for RUNX1-deficient tumors if the hypothesized link to breast cancer is correct.

Intersection of FOXO- and RUNX1-mediated gene expression programs in single breast epithelial cells during morphogenesis and tumor progression

Gene expression networks are complicated by the assortment of regulatory factors that bind DNA and modulate transcription combinatorially. Single-cell measurements can reveal biological mechanisms hidden by population averages, but their value has not been fully explored in the context of mRNA regulation. Here, we adapted a single-cell expression profiling technique to examine the gene expression program downstream of Forkhead box O (FOXO) transcription factors during 3D breast epithelial acinar morphogenesis. By analyzing patterns of mRNA fluctuations among individual matrix-attached epithelial cells, we found that a subset of FOXO target genes was jointly regulated by the transcription factor Runt-related transcription factor 1 (RUNX1). Knockdown of RUNX1 causes hyperproliferation and abnormal morphogenesis, both of which require normal FOXO function. Down-regulating RUNX1 and FOXOs simultaneously causes widespread oxidative stress, which arrests proliferation and restores normal acinar morphology. In hormone-negative breast cancers lacking human epidermal growth factor receptor 2 (HER2) amplification, we find that RUNX1 down-regulation is strongly associated with up-regulation of FOXO1, which may be required to support growth of RUNX1-negative tumors. The coordinate function of these two tumor suppressors may provide a failsafe mechanism that inhibits cancer progression.

One-step purification of nucleic acid for gene expression analysis via Immiscible Filtration Assisted by Surface Tension (IFAST)

The extraction and purification of nucleic acids from complex samples (e.g. blood, biopsied tissue, cultured cells, food) is an essential prerequisite for many applications in biology including genotyping, transcriptional analysis, systems biology, epigenetic analysis, and virus/bacterial detection. In this report, we describe a new process of nucleic acid extraction that utilizes "pinned" aqueous/organic liquid interfaces in microchannels to streamline the extraction mechanism, replacing all washing steps with a single traverse of an immiscible fluid barrier, termed Immiscible Filtration Assisted by Surface Tension (IFAST). Nucleic acids in biological samples are bound to paramagnetic particles and then drawn across the IFAST device (or array of IFAST devices) using a magnet. While the strength of the IFAST barrier is suitable for separation of nucleic acids from lysate in its current embodiment, its permeability can be selectively adapted by adjusting the surface tensions/energies associated with the cell lysate, the immiscible phase, and the device surface, enabling future expansion to other non-nucleic acid applications. Importantly, processing time is reduced from 15-45 minutes to less than 5 minutes while maintaining purity, yield, and scalability equal to or better than prevailing methods. Operation is extremely simple and no additional lab infrastructure is required. The IFAST technology thus significantly enhances researchers' abilities to isolate and analyze nucleic acids, a process which is critical and ubiquitous in an extensive array of scientific fields.

Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast

We discovered that the relative durations of the phases of the yeast metabolic cycle change with the growth rate. These changes can explain mechanistically the transcriptional growth-rate responses of all yeast genes (25% of the genome) that we find to be the same across all studied nutrient limitations in either ethanol or glucose media.

We studied the steady-state responses to changes in growth rate of yeast when ethanol is the sole source of carbon and energy. Analysis of these data, together with data from studies where glucose was the carbon source, allowed us to distinguish a “universal” growth rate response (GRR) common to all media studied from a GRR specific to the carbon source. Genes with positive universal GRR include ribosomal, translation, and mitochondrial genes, and those with negative GRR include autophagy, vacuolar, and stress response genes. The carbon source–specific GRR genes control mitochondrial function, peroxisomes, and synthesis of vitamins and cofactors, suggesting this response may reflect the intensity of oxidative metabolism. All genes with universal GRR, which comprise 25% of the genome, are expressed periodically in the yeast metabolic cycle (YMC). We propose that the universal GRR may be accounted for by changes in the relative durations of the YMC phases. This idea is supported by oxygen consumption data from metabolically synchronized cultures with doubling times ranging from 5 to 14 h. We found that the high oxygen consumption phase of the YMC can coincide exactly with the S phase of the cell division cycle, suggesting that oxidative metabolism and DNA replication are not incompatible.

Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells

Human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs) are promising candidate cell sources for regenerative medicine. However, despite the common ability of hiPSCs and hESCs to differentiate into all 3 germ layers, their functional equivalence at the single cell level remains to be demonstrated. Moreover, single cell heterogeneity amongst stem cell populations may underlie important cell fate decisions. Here, we used single cell analysis to resolve the gene expression profiles of 362 hiPSCs and hESCs for an array of 42 genes that characterize the pluripotent and differentiated states. Comparison between single hESCs and single hiPSCs revealed markedly more heterogeneity in gene expression levels in the hiPSCs, suggesting that hiPSCs occupy an alternate, less stable pluripotent state. hiPSCs also displayed slower growth kinetics and impaired directed differentiation as compared with hESCs. Our results suggest that caution should be exercised before assuming that hiPSCs occupy a pluripotent state equivalent to that of hESCs, particularly when producing differentiated cells for regenerative medicine aims.

Origins of regulated cell-to-cell variability

Single-cell measurements and lineage-tracing experiments are revealing that phenotypic cell-to-cell variability is often the result of deterministic processes, despite the existence of intrinsic noise in molecular networks. In most cases, this determinism represents largely uncharacterized molecular regulatory mechanisms, which places the study of cell-to-cell variability in the realm of molecular cell biology. Further research in the field will be important to advance quantitative cell biology because it will provide new insights into the mechanisms by which cells coordinate their intracellular activities in the spatiotemporal context of the multicellular environment.

Functional genomics of the brain: uncovering networks in the CNS using a systems approach

The central nervous system (CNS) is undoubtedly the most complex human organ system in terms of its diverse functions, cellular composition, and connections. Attempts to capture this diversity experimentally were the foundation on which the field of neurobiology was built. Until now though, techniques were either painstakingly slow or insufficient in capturing this heterogeneity. In addition, the combination of multiple layers of information needed for a complete picture of neuronal diversity from the epigenome to the proteome requires an even more complex compilation of data. In this era of high-throughput genomics though, the ability to isolate and profile neurons and brain tissue has increased tremendously and now requires less effort. Both microarrays and next-generation sequencing have identified neuronal transcriptomes and signaling networks involved in normal brain development, as well as in disease. However, the expertise needed to organize and prioritize the resultant data remains substantial. A combination of supervised organization and unsupervised analyses are needed to fully appreciate the underlying structure in these datasets. When utilized effectively, these analyses have yielded striking insights into a number of fundamental questions in neuroscience on topics ranging from the evolution of the human brain to neuropsychiatric and neurodegenerative disorders. Future studies will incorporate these analyses with behavioral and physiological data from patients to more efficiently move toward personalized therapeutics.

Accurate expression profiling of very small cell populations

BACKGROUND

Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts.

METHODS AND FINDINGS

We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles.

CONCLUSIONS

Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells.

Quantification and cell-to-cell variation of vascular endothelial growth factor receptors

The vascular endothelial growth factor receptors (VEGFR) play a significant role in angiogenesis, the formation of new blood vessels from existing vasculature. Systems biology offers promising approaches to better understand angiogenesis by computational modeling the key molecular interactions in this process. Such modeling requires quantitative knowledge of cell surface density of pro-angiogenic receptors versus anti-angiogenic receptors, their regulation, and their cell-to-cell variability. Using quantitative fluorescence, we systematically characterized the endothelial surface density of VEGFRs and neuropilin-1 (NRP1). We also determined the role of VEGF in regulating the surface density of these receptors. Applying cell-by-cell analysis revealed heterogeneity in receptor surface density and VEGF tuning of this heterogeneity. Altogether, we determine inherent differences in the surface expression levels of these receptors and the role of VEGF in regulating the balance of anti-angiogenic or modulatory (VEGFR1) and pro-angiogenic (VEGFR2) receptors.

Activation of retinoic acid receptor signaling coordinates lineage commitment of spontaneously differentiating mouse embryonic stem cells in embryoid bodies

Retinoid signaling has been implicated in embryonic stem cell differentiation. Here we present a systematic analysis of gene expression changes in mouse embryonic stem cells (mESCs), during their spontaneous differentiation into embryoid bodies and the effect of all-trans retinoic acid (ATRA) on this process. We show that retinoic acid is present in the serum and is sufficient to activate retinoid signaling at a basal level in undifferentiated mESCs. This signal disappears during embryoid body formation. However exogenously added ATRA resets the spontaneous differentiation programs in embryoid bodies and initiates a distinct genetic program. These data suggest that retinoid signaling not only promotes a particular pathway but also acts as a context dependent general coordinator of the differentiation states in embryonic stem cells.

Not all noise is waste

Stochastic profiling, a method to rank heterogeneity of gene expression in a cell population, shows that quantifying cell-to-cell variability has come of age and leads to biological insight.