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

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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.

Complex regulation of ADAR-mediated RNA-editing across tissues.

BACKGROUND: RNA-editing is a tightly regulated, and essential cellular process for a properly functioning brain. Dysfunction of A-to-I RNA editing can have catastrophic effects, particularly in the central nervous system. Thus, understanding how the process of RNA-editing is regulated has important implications for human health. However, at present, very little is known about the regulation of editing across tissues, and individuals. RESULTS: Here we present an analysis of RNA-editing patterns from 9 different tissues harvested from a single mouse. For comparison, we also analyzed data for 5 of these tissues harvested from 15 additional animals. We find that tissue specificity of editing largely reflects differential expression of substrate transcripts across tissues. We identified a surprising enrichment of editing in intronic regions of brain transcripts, that could account for previously reported higher levels of editing in brain. There exists a small but remarkable amount of editing which is tissue-specific, despite comparable expression levels of the edit site across multiple tissues. Expression levels of editing enzymes and their isoforms can explain some, but not all of this variation. CONCLUSIONS: Together, these data suggest a complex regulation of the RNA-editing process beyond transcript expression levels.

The effect of sampling from subdivided populations on species identification with DNA barcodes using a Bayesian statistical approach.

Barcoding is an initiative to define a standard fragment of DNA to be used to assign sequences of unknown origin to existing known species whose sequences are recorded in databases. This is a difficult task when species are closely related and individuals of these species might have more than one origin. Using a previously introduced Bayesian statistical tree-less assignment algorithm based on segregating sites, we examine how it functions in the presence of hidden population subdivision with closely related species using simulations. Not surprisingly, adding samples to the database from a greater proportion of the species range leads to a consistently higher number of accurate results. Without such samples, query sequences that originate from outside of the sampled range are easily misinterpreted as coming from other species. However, we show that even the addition of a single sample from a different subpopulation is sufficient to greatly increase the probability of placement of unknown queries into the correct species group. This study highlights the importance of broad sampling, even with five reference samples per species, in the creation of a reference database.

Assigning sequences to species in the absence of large interspecific differences.

Barcoding is an initiative to define a standard fragment of DNA to be used to assign unknown sequences to existing known species groups that have been pre-identified externally (by a taxonomist). Several methods have been described that attempt to place this assignment into a Bayesian statistical framework. Here we describe an algorithm that makes use of segregating sites and we examine how well these methods perform in the absence of an interspecific 'barcoding gap'. When a barcoding gap exists, that is when the data are clearly delimited, most methods perform well. Here we have used data from the Drosophila genus because this genus includes sibling species and the species relationships within this species while complex are, arguably, better understood than in any other group. The results show that the Bayesian methods perform well even in the absence of a barcoding gap. The sequences from Drosophila are correctly identified and only when the degree of incomplete lineage sorting is extreme in simulations or within the Drosophila species, do they fail in their identifications and even then, the "correct" species has a high posterior probability.