Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex.

We constructed error-correcting DNA barcodes that allow one run of a massively parallel pyrosequencer to process up to 1,544 samples simultaneously. Using these barcodes we processed bacterial 16S rRNA gene sequences representing microbial communities in 286 environmental samples, corrected 92% of sample assignment errors, and thus characterized nearly as many 16S rRNA genes as have been sequenced to date by Sanger sequencing.

Geobiology of a microbial endolithic community in the Yellowstone geothermal environment.

The endolithic environment, the pore space of rocks, is a ubiquitous habitat for microorganisms on the Earth and is an important target of the search for life elsewhere in the Solar System. Photosynthetic, endolithic microbial communities commonly inhabit the outer millimetres to centimetres of all rocks exposed to the Earth's surface. In the most extreme terrestrial climates, such as hot and cold deserts, endolithic microorganisms are often the main form of life. The endolithic microhabitat gives protection from intense solar radiation and desiccation, and it provides mineral nutrients, rock moisture and growth surfaces. Here we describe the discovery and identification of the constituents of an extremely acidic (pH 1) endolithic microbial community inhabiting the pore space of rocks in the geothermal environment of Yellowstone National Park, USA. Subjected to silica mineralization, such endolithic communities constitute biomarkers that can become fossilized and potentially preserved in the geological record. Remnants of these communities could serve as biosignatures and provide important clues about ancient life associated with geothermal environments on the Earth or elsewhere in the Solar System.

Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat.

To investigate the extent of genetic stratification in structured microbial communities, we compared the metagenomes of 10 successive layers of a phylogenetically complex hypersaline mat from Guerrero Negro, Mexico. We found pronounced millimeter-scale genetic gradients that were consistent with the physicochemical profile of the mat. Despite these gradients, all layers displayed near-identical and acid-shifted isoelectric point profiles due to a molecular convergence of amino-acid usage, indicating that hypersalinity enforces an overriding selective pressure on the mat community.

Phylogenetic stratigraphy in the Guerrero Negro hypersaline microbial mat.

The microbial mats of Guerrero Negro (GN), Baja California Sur, Mexico historically were considered a simple environment, dominated by cyanobacteria and sulfate-reducing bacteria. Culture-independent rRNA community profiling instead revealed these microbial mats as among the most phylogenetically diverse environments known. A preliminary molecular survey of the GN mat based on only ∼1500 small subunit rRNA gene sequences discovered several new phylum-level groups in the bacterial phylogenetic domain and many previously undetected lower-level taxa. We determined an additional ∼119,000 nearly full-length sequences and 28,000 >200 nucleotide 454 reads from a 10-layer depth profile of the GN mat. With this unprecedented coverage of long sequences from one environment, we confirm the mat is phylogenetically stratified, presumably corresponding to light and geochemical gradients throughout the depth of the mat. Previous shotgun metagenomic data from the same depth profile show the same stratified pattern and suggest that metagenome properties may be predictable from rRNA gene sequences. We verify previously identified novel lineages and identify new phylogenetic diversity at lower taxonomic levels, for example, thousands of operational taxonomic units at the family-genus levels differ considerably from known sequences. The new sequences populate parts of the bacterial phylogenetic tree that previously were poorly described, but indicate that any comprehensive survey of GN diversity has only begun. Finally, we show that taxonomic conclusions are generally congruent between Sanger and 454 sequencing technologies, with the taxonomic resolution achieved dependent on the abundance of reference sequences in the relevant region of the rRNA tree of life.

Advances in human proteomics at high scale with the SOMAscan proteomics platform.

In 1997, while still working at NeXstar Pharmaceuticals, several of us made a proteomic bet. We thought then, and continue to think, that proteomics offers a chance to identify disease-specific biomarkers and improve healthcare. However, interrogating proteins turned out to be a much harder problem than interrogating nucleic acids. Consequently, the 'omics' revolution has been fueled largely by genomics. High-scale proteomics promises to transform medicine with personalized diagnostics, prevention, and treatment. We have now reached into the human proteome to quantify more than 1000 proteins in any human matrix - serum, plasma, CSF, BAL, and also tissue extracts - with our new SOMAmer-based proteomics platform. The surprising and pleasant news is that we have made unbiased protein biomarker discovery a routine and fast exercise. The downstream implications of the platform are substantial.

Aptamers and the RNA world, past and present.

Aptamers and the SELEX process were discovered over two decades ago. These discoveries have spawned a productive academic and commercial industry. The collective results provide insights into biology, past and present, through an in vitro evolutionary exploration of the nature of nucleic acids and their potential roles in ancient life. Aptamers have helped usher in an RNA renaissance. Here we explore some of the evolution of the aptamer field and the insights it has provided for conceptualizing an RNA world, from its nascence to our current endeavor employing aptamers in human proteomics to discover biomarkers of health and disease.

Protein signature of lung cancer tissues.

Lung cancer remains the most common cause of cancer-related mortality. We applied a highly multiplexed proteomic technology (SOMAscan) to compare protein expression signatures of non small-cell lung cancer (NSCLC) tissues with healthy adjacent and distant tissues from surgical resections. In this first report of SOMAscan applied to tissues, we highlight 36 proteins that exhibit the largest expression differences between matched tumor and non-tumor tissues. The concentrations of twenty proteins increased and sixteen decreased in tumor tissue, thirteen of which are novel for NSCLC. NSCLC tissue biomarkers identified here overlap with a core set identified in a large serum-based NSCLC study with SOMAscan. We show that large-scale comparative analysis of protein expression can be used to develop novel histochemical probes. As expected, relative differences in protein expression are greater in tissues than in serum. The combined results from tissue and serum present the most extensive view to date of the complex changes in NSCLC protein expression and provide important implications for diagnosis and treatment.

The stability of the circulating human proteome to variations in sample collection and handling procedures measured with an aptamer-based proteomics array.

Blood-based protein biomarkers hold great promise to advance medicine with applications that detect and diagnose diseases and aid in their treatment. We are developing such applications with our proteomics technology that combines high-content with low limits of detection. Biomarker discovery relies heavily on archived blood sample collections. Blood is dynamic and changes with different sampling procedures potentially confounding biomarker studies. In order to better understand the effects of sampling procedures on the circulating proteome, we studied three sample collection variables commonly encountered in archived sample sets. These variables included (1) three different sample tube types, PPT plasma, SST serum, and Red Top serum, (2) the time from venipuncture to centrifugation, and (3) the time from centrifugation to freezing. We profiled 498 proteins for each of 240 samples and compared the results by ANOVA. The results found no significant variation in the measurements for most proteins (approximately 99%) when the two sample processing times tested were 2h or less, regardless of sample tube type. Even at the longest timepoints, 20 h, approximately 82% of the proteins, on average for the three collection tube types, showed no significant change. These results are encouraging for proteomic biomarker discovery.

High-content affinity-based proteomics: unlocking protein biomarker discovery.

Single protein biomarkers measured with antibody-based affinity assays are the basis of molecular diagnostics in clinical practice today. There is great hope in discovering new protein biomarkers and combinations of protein biomarkers for advancing medicine through monitoring health, diagnosing disease, guiding treatment, and developing new therapeutics. The goal of high-content proteomics is to unlock protein biomarker discovery by measuring many (thousands) or all (∼23,000) proteins in the human proteome in an unbiased, data-driven approach. High-content proteomics has proven technically difficult due to the diversity of proteins, the complexity of relevant biological samples, such as blood and tissue, and large concentration ranges (in the order of 10(12) in blood). Mass spectrometry and affinity methods based on antibodies have dominated approaches to high-content proteomics. For technical reasons, neither has achieved adequate simultaneous performance and high-content. Here we review antibody-based protein measurement, multiplexed antibody-based protein measurement, and limitations of antibodies for high-content proteomics due to their inherent cross-reactivity. Finally, we review a new affinity-based proteomic technology developed from the ground up to solve the problem of high content with high sensitivity and specificity. Based on a new generation of slow off-rate modified aptamers (SOMAmers), this technology is unlocking biomarker discovery.

Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer.

BACKGROUND: Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥ 10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC. CONCLUSIONS/SIGNIFICANCE: This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer.