Author Correction: Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Extensive genetic variation in somatic human tissues.

Genetic variation between individuals has been extensively investigated, but differences between tissues within individuals are far less understood. It is commonly assumed that all healthy cells that arise from the same zygote possess the same genomic content, with a few known exceptions in the immune system and germ line. However, a growing body of evidence shows that genomic variation exists between differentiated tissues. We investigated the scope of somatic genomic variation between tissues within humans. Analysis of copy number variation by high-resolution array-comparative genomic hybridization in diverse tissues from six unrelated subjects reveals a significant number of intraindividual genomic changes between tissues. Many (79%) of these events affect genes. Our results have important consequences for understanding normal genetic and phenotypic variation within individuals, and they have significant implications for both the etiology of genetic diseases such as cancer and for immortalized cell lines that might be used in research and therapeutics.

Personal omics profiling reveals dynamic molecular and medical phenotypes.

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Performance comparison of whole-genome sequencing platforms.

Whole-genome sequencing is becoming commonplace, but the accuracy and completeness of variant calling by the most widely used platforms from Illumina and Complete Genomics have not been reported. Here we sequenced the genome of an individual with both technologies to a high average coverage of ∼76×, and compared their performance with respect to sequence coverage and calling of single-nucleotide variants (SNVs), insertions and deletions (indels). Although 88.1% of the ∼3.7 million unique SNVs were concordant between platforms, there were tens of thousands of platform-specific calls located in genes and other genomic regions. In contrast, 26.5% of indels were concordant between platforms. Target enrichment validated 92.7% of the concordant SNVs, whereas validation by genotyping array revealed a sensitivity of 99.3%. The validation experiments also suggested that >60% of the platform-specific variants were indeed present in the genome. Our results have important implications for understanding the accuracy and completeness of the genome sequencing platforms.

The complete genome sequence of Roseobacter denitrificans reveals a mixotrophic rather than photosynthetic metabolism.

Purple aerobic anoxygenic phototrophs (AAPs) are the only organisms known to capture light energy to enhance growth only in the presence of oxygen but do not produce oxygen. The highly adaptive AAPs compose more than 10% of the microbial community in some euphotic upper ocean waters and are potentially major contributors to the fixation of the greenhouse gas CO2. We present the complete genomic sequence and feature analysis of the AAP Roseobacter denitrificans, which reveal clues to its physiology. The genome lacks genes that code for known photosynthetic carbon fixation pathways, and most notably missing are genes for the Calvin cycle enzymes ribulose bisphosphate carboxylase (RuBisCO) and phosphoribulokinase. Phylogenetic evidence implies that this absence could be due to a gene loss from a RuBisCO-containing alpha-proteobacterial ancestor. We describe the potential importance of mixotrophic rather than autotrophic CO2 fixation pathways in these organisms and suggest that these pathways function to fix CO2 for the formation of cellular components but do not permit autotrophic growth. While some genes that code for the redox-dependent regulation of photosynthetic machinery are present, many light sensors and transcriptional regulatory motifs found in purple photosynthetic bacteria are absent.