Markedly heterogeneous COVID-19 testing plans among US colleges and universities

As the COVID-19 pandemic worsens in the United States [1], colleges that have invited students back for the fall are finalizing mitigation plans to lessen the spread of SARS-CoV-2. Even though students have largely been away from campuses over the summer, several outbreaks associated with colleges have already occurred [2], foreshadowing the scale of infection that could result from hundreds of thousands of students returning to college towns and cities. While many institutions have released return-to-campus plans designed to reduce viral spread and to rapidly identify outbreaks should they occur, in many cases communications by college administrators have been opaque. To contribute to an evaluation of university preparedness for the COVID-19 pandemic, we assessed a crucial element: COVID-19 on-campus testing. We examined testing plans at more than 500 colleges and universities throughout the US, and collated statistics, as well as narratives from publicly facing websites. We discovered a highly variable and muddled state of COVID-19 testing plans among US institutions of higher education that has been shaped by discrepancies between scientific studies and federal guidelines. We highlight cases of divergence between university testing plans and public health best practices, as well as potential bioethical issues.

Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1, 2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens.

Fast and accurate diagnostics from highly multiplexed sequencing assays

Scalable, inexpensive, accurate, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays that rely on high-throughput sequencing (HMSAs) can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, the analysis and interpretation of HMSAs requires overcoming several computational and statistical challenges. Using recently acquired experimental data, we present and validate an accurate and fast computational testing workflow based on kallisto and bustools, that utilize robust statistical methods and fast, memory efficient algorithms for processing 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 HMSAs.

log(x+1)* and log(1+x)

Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses dont hold when examining low-expressed genes, with the result that standard workflows can produce misleading results. Key PointsLowly expressed genes in single-cell RNA-seq can be easliy misanalyzed. log(1+x) count normalization introduces errors for lowly expressed genes The average log(1+x) expression differs considerably from log(x) when x is small An alternative approach is to use the fraction of cells with non-zero expression