Alan D. Stern

Cell size assays for mass cytometry

Mass cytometry offers the advantage of allowing the simultaneous measurement of a greater number parameters than conventional flow cytometry. However, to date, mass cytometry has lacked a reliable alternative to the light scatter properties that are commonly used as a cell size metric in flow cytometry (forward scatter intensity—FSC). Here, we report the development of two plasma membrane staining assays to evaluate mammalian cell size in mass cytometry experiments. One is based on wheat germ agglutinin (WGA) staining and the other on Osmium tetroxide (OsO4) staining, both of which have preferential affinity for cell membranes. We first perform imaging and flow cytometry experiments to establish a relationship between WGA staining intensity and traditional measures of cell size. We then incorporate WGA staining in mass cytometry analysis of human whole blood and show that WGA staining intensity has reproducible patterns within and across immune cell subsets that have distinct cell sizes. Lastly, we stain PBMCs or dissociated lung tissue with both WGA and OsO4 ; mass cytometry analysis demonstrates that the two staining intensities correlate well with one another. We conclude that both WGA and OsO4 may be used to acquire cell size‐related parameters in mass cytometry experiments, and expect these stains to be broadly useful in expanding the range of parameters that can be measured in mass cytometry experiments.

Drug response consistency in CCLE and CGP

The Cancer Cell Line Encyclopedia1 (CCLE) and Cancer Genome Project2 (CGP) are two independent large-scale efforts to characterize genomes, mRNA expression, and anti-cancer drug dose–responses across cell lines, providing a public resource relating cellular biochemical context to drug sensitivity. A recent study3 analysed correlations between reported dose–response metrics and found inconsistency between CCLE and CGP, thus questioning the validity of not only these, but also other current and future costly large-scale studies. Here, we examine two metrics of drug responsiveness (slope and area under the curve) that we derive from the original CCLE and CGP data, and find reasonable and statistically significant consistency. Our results revive confidence that the CCLE and CGP drug dose–response data are of sufficient quality for meaningful analyses. There is a Reply to this Comment by Safikhani, Z. et al. Nature 540, http://dx.doi.org/10.1038/nature20581 (2016). CCLE and CGP share 2,520 dose–responses across 285 cell lines and 15 drugs, but cells were treated with different dose ranges. To compare

Commercial space flight is a game-changer

Next month, SpaceX, an aerospace company in Hawthorne, California, is scheduled to launch the first cargo resupply mission by a commercial space company to the International Space Station (ISS). Its Falcon 9 orbital rocket will send to the station a Dragon capsule stocked with food, water and other astronaut provisions. The flight will be the first of many resupply missions, under contract by NASA to SpaceX in a deal worth around US

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

1.6 billion. But more importantly, it represents the entry of commercial space companies into the big league. It will place SpaceX at the heart of ISS operations and will open up important capabilities for science by increasing the number of future science experiments aboard the station and providing a way to bring samples produced in microgravity back to Earth. The flight is a watershed, but it is just the beginning of the potentially game-changing capabilities and economic promise of the emerging commercial space industry for science. Take the realm of suborbital flight — missions that stay just a short time in space. It has been used effectively by researchers around the world for more than 60 years to test new techniques and technologies, conduct special-purpose observations and train students. But the concept is about to undergo a reboot, thanks to commercial firms such as Virgin Galactic in Las Cruces, New Mexico, and Blue Origin in Seattle, Washington, along with less well known but equally interesting entrants XCOR Aerospace and Masten Space Systems in Mojave, California, and Armadillo Aerospace in Heath, Texas. The companies will revolutionize suborbital access by lowering costs to a tenth of those today by flying reusable rather than throw-away space vehicles. Together, these firms will vastly increase access to microgravity for scientists, instrument technology testers and educators, in much the same way that personal computers expanded access to computing in the 1980s from the mainframe machines of the 1970s. And commercial space companies offer science capabilities and options at more than just the low altitudes at which the station and suborbital vehicles fly. The Google Lunar X Prize is spurring companies such as Moon Express in San Francisco, California, backed by deeppocketed Internet moguls, to offer flights to the Moon for cut-rate prices. How? Moon Express and its competitors hope to build a twentyfirst-century robotic space business niche by amalgamating payloads from various universities, labs and countries, and sharing the costs. They are betting that although few countries and private entities can afford the one to two hundred million dollars to mount a lunar mission, many can afford to share the cost with half a dozen others, thereby reducing the cost of lunar missions to perhaps a few tens of millions of dollars — or even less — for small payloads. Space firms also have attractive deals for science in Earth orbit. For space experiments needing short stays (weeks to months), SpaceX is offering cut-rate access to orbit aboard Dragon, and the opportunity for later re-flight. Bigelow Aerospace of Las Vegas, Nevada, intends to take the commercial space concept to a new level — by constructing a fully functional orbiting lab that could rival the available volume and crew complement of the ISS. Bigelow’s station will give private companies and the 150 or so smaller countries that are not a part of the ISS consortium the capability to fly experiments and experimenters for stays of three months or longer — perhaps even years. And although pricing is still in flux, Bigelow hopes that mission prices will be less than what small science satellites cost today. Some of these nascent ventures may be successful, others not. And some are less conventional than others. Perhaps the most conventional, and most game-changing in the long run, is SpaceX’s promise of Falcon rocket launches at costs of

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

55 million that have the same capabilities as rockets now offered almost exclusively at costs of

Network Reconstruction from Perturbation Time Course Data

150 million or more. Those lower prices have caught the eye, and the purse, of big-name communications-satellite suppliers — such as SES, based in Betzdorf, Luxembourg; Iridium in McLean, Virginia; and Orbcomm in Fort Lee, New Jersey — who have showered SpaceX with contracts for more than a dozen launches. It is unlikely to be long before science agencies such as NASA and the European Space Agency, which are feeling the simultaneous pinch of cost overruns and budget squeezes, begin to make similar contracts. Over a series of missions, a saving of

Validating Antibodies for Quantitative Western Blot Measurements with Microwestern Array

100-million per launch could add up to more than a billion extra dollars in the bank — and that, in turn, could result in science missions that might otherwise not have been possible. Who says that commercial space flight is possible only for wealthy space tourists and communications-satellite operators? Commercial space ventures will provide scientists with much-needed and welcome new ways to advance their research by making space easier and cheaper to access. ■ SEE EDITORIAL P.415 AND NEWS P.426

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this context, synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD. AKT dynamics explain S-phase entry synergy between EGF and insulin, but stochastic ERK dynamics seem to drive cell-to-cell proliferation variability, which in simulations are predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations predict MEK alteration negligibly influences transformation, consistent with clinical data. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, moving towards more rational cancer combination therapy.

Fluorescence Multiplexing with Spectral Imaging and Combinatorics

Transcriptomic data are widely available, and the extent to which they are predictive of protein abundances remains debated. Using multiple public databases, we calculate mRNA and mRNA-to-protein ratio variability across human tissues to quantify and classify genes for protein abundance predictability confidence. We propose that such predictability is best understood as a spectrum. A gene-specific, tissue-independent mRNA-to-protein ratio plus mRNA levels explains ∼80% of protein abundance variance for more predictable genes, as compared to ∼55% for less predictable genes. Protein abundance predictability is consistent with independent mRNA and protein data from two disparate cell lines, and mRNA-to-protein ratios estimated from publicly-available databases have predictive power in these independent datasets. Genes with higher predictability are enriched for metabolic function, tissue development/cell differentiation roles, and transmembrane transporter activity. Genes with lower predictability are associated with cell adhesion, motility and organization, the immune system, and the cytoskeleton. Surprisingly, many genes that regulate mRNA-to-protein ratios are constitutively expressed but also exhibit ratio variability, suggesting a general autoregulation mechanism whereby protein expression profile changes can be implemented quickly, or homeostatic sensing stabilizes protein abundances under fluctuating conditions. Gene classifications and their mRNA-to-protein ratios are provided as a resource to facilitate protein abundance predictions by others.

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Network reconstruction is an important objective for understanding biological interactions and their role in disease mechanisms and treatment. Yet, even for small systems, contemporary reconstruction methods struggle with critical network properties: (i) edge causality, sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; and (iv) environment-specific effects. Moreover, experimental noise significantly impedes many methods. We report an approach that addresses the aforementioned challenges to robustly and uniquely infer edge weights from sparse perturbation time course data that formally requires only one perturbation per node. We apply this approach to randomized 2 and 3 node systems with varied and complex dynamics as well as to a family of 16 non-linear feedforward loop motif models. In each case, we find that it can robustly reconstruct the networks, even with highly noisy data in some cases. Surprisingly, the results suggest that incomplete perturbation (e.g. 50% knockdown vs. knockout) is often more informative than full perturbation, which may fundamentally change experimental strategies for network reconstruction. Systematic application of this method can enable unambiguous network reconstruction, and therefore better prediction of cellular responses to perturbations such as drugs. The method is general and can be applied to any network inference problem where perturbation time course experiments are possible.