Reliable and accurate diagnostics from highly multiplexed sequencing assays

Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA

A systematic comparison of error correction enzymes by next-generation sequencing

Abstract Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality and cost of gene synthesis are limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment. Here, we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in model gene assemblies and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G transversions whereas T7 Endonuclease I preferentially corrects A/T transversions. More generally, this experimental and computational pipeline is a fast, scalable and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods

Reliable and accurate diagnostics from highly multiplexed sequencing assays

Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA

DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions.

Multiplexed assays allow functional testing of large synthetic libraries of genetic elements, but are limited by the designability, length, fidelity and scale of the input DNA. Here, we improve DropSynth, a low-cost, multiplexed method that builds gene libraries by compartmentalizing and assembling microarray-derived oligonucleotides in vortexed emulsions. By optimizing enzyme choice, adding enzymatic error correction and increasing scale, we show that DropSynth can build thousands of gene-length fragments at >20% fidelity

Activation of peripheral nerve fibers by electrical stimulation in the sole of the foot

BACKGROUND: Human nociceptive withdrawal reflexes (NWR) can be evoked by electrical stimulation applied to the sole of the foot. However, elicitation of NWRs is highly site dependent, and NWRs are especially difficult to elicit at the heel. The aim of the present study was to investigate potential peripheral mechanisms for any site dependent differences in reflex thresholds. RESULTS: The first part of the study investigated the neural innervation in different sites of the sole of the foot using two different staining techniques. 1) Staining for the Na(v)1.7 antigen (small nociceptive fibers) and 2) the Sihler whole nerve technique (myelinated part of the nerve). No differences in innervation densities were found across the sole of the foot using the two staining techniques: Na(v)1.7 immunochemistry (small nociceptive fibers (1-way ANOVA, NS)) and the Sihler’s method (myelinated nerve fibers (1-way ANOVA, NS)). However, the results indicate that there are no nociceptive intraepidermal nerve fibers (IENFs) innervating the heel. Secondly, mathematical modeling was used to investigate to what degree differences in skin thicknesses affect the activation thresholds of Aδ and Aβ fibers in the sole of the foot. The modeling comprised finite element analysis of the volume conduction combined with a passive model of the activation of branching cutaneous nerve fibers. The model included three different sites in the sole of the foot (forefoot, arch and heel) and three different electrode sizes (diameters: 9.1, 12.9, and 18.3 mm). For each of the 9 combinations of site and electrode size, a total of 3000 Aβ fibers and 300 Aδ fibers was modeled. The computer simulation of the effects of skin thicknesses and innervation densities on thresholds of modeled Aδ and Aβ fibers did not reveal differences in pain and perception thresholds across the foot sole as have been observed experimentally. Instead a lack of IENFs at the heel decreased the electrical activation thresholds compared to models including IENFs. CONCLUSIONS: The nerve staining and modeling results do not explain differences in NWR thresholds across the sole of the foot which may suggest that central mechanisms contribute to variation in NWR excitability across the sole of the foot

A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing.

G protein-coupled receptors (GPCRs) are central to how mammalian cells sense and respond to chemicals. Mammalian olfactory receptors (ORs), the largest family of GPCRs, mediate the sense of smell through activation by small molecules, though for most bonafide ligands, they have not been identified. Here, we introduce a platform to screen large chemical panels against multiplexed GPCR libraries using next-generation sequencing of barcoded genetic reporters in stably engineered human cell lines. We mapped 39 mammalian ORs against 181 odorants and identified 79 interactions that have not been reported to our knowledge, including ligands for 15 previously orphaned receptors. This multiplexed receptor assay allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale

Structural and functional characterization of G protein-coupled receptors with deep mutational scanning.

The >800 human G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state- and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here, we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G protein signal transduction. We tested 7800 of 7828 possible single amino acid substitutions to the beta-2 adrenergic receptor (β2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for β2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we identify residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors