Biology subjects

Ackerman, C. M.

Publications and source records attributed to Ackerman, C. M..

3 recordsLinked to original sources

bioRxiv2021

Multiplexed detection of bacterial nucleic acids using Cas13 in droplet microarrays

Rapid and accurate diagnosis of infections is fundamental to individual patient care and public health management. Nucleic acid detection methods are critical to this effort, but are limited either in the breadth of pathogens targeted or by the expertise and infrastructure required. We present here a high-throughput system that enables rapid identification of bacterial pathogens, bCARMEN, which utilizes: (1) modular CRISPR-Cas13-based nucleic acid detection with enhanced sensitivity and specificity; and (2) a droplet microfluidic system that enables thousands of simultaneous, spatially multiplexed detection reactions at nanoliter volumes; and (3) a novel pre-amplification strategy that further enhances sensitivity and specificity. We demonstrate bCARMEN is capable of detecting and discriminating 52 clinically relevant bacterial species and several key antibiotic resistance genes. We further develop a proof of principle system for use with stabilized reagents and a simple workflow with optical readout using a cell phone camera, opening up the possibility of a rapid point-of-care multiplexed bacterial pathogen identification and antibiotic susceptibility testing. Significance StatementIn this paper, we use a novel primer design method combined with droplet-based CRISPR Cas13 detection to distinguish 52 clinically relevant bacterial pathogens in a single assay. We also apply the method to detect and distinguish a panel of major antibiotic resistance genes, which is of critical importance in this era of rising antibiotic resistance. Finally, we make key advances towards making our diagnostic assay deployable at the point-of-care, with a simplified emulsion-free assay process that uses mobile phone camera for detection and reduces infrastructure/skilled labor requirements.

microbiology↗

bioRxiv2020

Efficient design of maximally active and specific nucleic acid diagnostics for thousands of viruses

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data--yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic-target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a viruss genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPTs designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPTs designs exhibit significantly higher fluorescence and permit lower limits of detection, across a viruss entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

genomics↗

bioRxiv2020

Arbitrary Boolean logical search operations on massive molecular file systems

DNA is an ultra-high-density storage medium that could meet exponentially growing worldwide demand for archival data storage if DNA synthesis costs declined sufficiently and random access of files within exabyte-to-yottabyte-scale DNA data pools were feasible. To overcome the second barrier, here we encapsulate data-encoding DNA file sequences within impervious silica capsules that are surface-labeled with single-stranded DNA barcodes. Barcodes are chosen to represent file metadata, enabling efficient and direct selection of sets of files with Boolean logic. We demonstrate random access of image files from an image database using fluorescence sorting with selection sensitivity of 1 in 106 files, which thereby enables 1 in 106N per N optical channels. Our strategy thereby offers retrieval of random file subsets from exabyte and larger-scale long-term DNA file storage databases, offering a scalable solution for random-access of archival files in massive molecular datasets.

bioengineering↗