Aach, J. | bioRxiv Science

bioRxiv2018

Barcoded oligonucleotides ligated on RNA amplified for multiplex and parallel in-situ analyses

We present Barcoded Oligonucleotides Ligated On RNA Amplified for Multiplexed and parallel In-Situ analysis (BOLORAMIS), a reverse-transcription (RT)-free method for spatially-resolved, targeted, in-situ RNA identification of single or multiple targets. For this proof of concept, we have profiled 154 distinct coding and small non-coding transcripts ranging in sizes 18 nucleotides in length and upwards, from over 200, 000 individual human induced pluripotent stem cells (iPSC) and demonstrated compatibility with multiplexed detection, enabled by fluorescent in-situ sequencing. We use BOLORAMIS data to identify differences in spatial localization and cell-to-cell expression heterogeneity. Our results demonstrate BOLORAMIS to be a generalizable toolset for targeted, in-situ detection of coding and small non-coding RNA for single or multiplexed applications.

cell biology↗

bioRxiv2017

Toward D-Peptide Biosynthesis: Elongation Factor P Enables Ribosomal Incorporation Of Consecutive D-Amino Acids

To maintain stereospecific biochemistry in cells, living organisms have evolved mechanisms to exclude D-amino acids (DAA) in their protein synthesis machinery, which also limits our exploration of the realm of mirror-image molecules. Here, we show that high affinity between EF-Tu and aminoacyl-tRNA promotes D-amino acid incorporation. More strikingly, Elongation Factor P efficiently resolves peptidyl transferase stalling between two consecutive D-amino acids, and hence enables the translation of D-peptides.

biochemistry↗

bioRxiv2016

Optimizing complex phenotypes through model-guided multiplex genome engineering

Optimization of complex phenotypes in engineered microbial strains has traditionally been accomplished by laboratory evolution. However, only a subset of the resulting mutations may affect the phenotype of interest and many others may have unintended effects. Multiplexed genome editing can complement evolutionary approaches by creating diverse combinations of targeted changes, but in both cases it remains challenging to identify which alleles influence the desired phenotype. We present a method for identifying a minimal set of genomic modifications that optimizes a complex phenotype by combining iterative cycles of multiplex genome engineering and predictive modeling. We applied our method to the 63-codon E. coli strain C321.{Delta}A, which has 676 mutations relative to its wild-type ancestor, and identified six single nucleotide mutations that together recover 59% of the fitness defect exhibited by the strain. The resulting optimized strain, C321.DA.opt, is an improved chassis for production of proteins containing non-standard amino acids. Our data reveal how multiple cycles of multiplex automated genome engineering (MAGE) and inexpensive sequencing can generate rich genotypic and phenotypic diversity that can be combined with linear regression techniques to quantify individual allelic effects. While laboratory evolution relies on enrichment as a proxy for allelic effect, our model-guided approach is less susceptible than enrichment to bias from population dynamics and recombination efficiency. We also show that the method can identify beneficial de novo mutations that arise adventitiously. Beyond improving the fitness of C321, {Delta}A, our work provides a proof-of-principle for high-throughput quantification of individual allelic effects which can be used with any method for generating targeted genotypic diversity.

synthetic biology↗