Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign.

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

Single-Cell Protein Profiling by Microdroplet Barcoding and Next-Generation Sequencing.

DNA barcoding of individual cells combined with next-generation sequencing enables high-throughput parallel analysis of biomolecules at the single-cell level. Encoding protein identity with DNA barcoding of specific antibody binders achieves sequencing-based protein quantitation by converting protein signals into DNA signals. Here, we describe how to prepare DNA-barcoded antibodies and connect protein identities to cellular identities using droplet microfluidics. This approach allows for multiplex single-cell protein analysis compatible with single-cell transcriptomic and mutational profiling methods.

Optimization of a heterologous mevalonate pathway through the use of variant HMG-CoA reductases.

Expression of foreign pathways often results in suboptimal performance due to unintended factors such as introduction of toxic metabolites, cofactor imbalances or poor expression of pathway components. In this study we report a 120% improvement in the production of the isoprenoid-derived sesquiterpene, amorphadiene, produced by an engineered strain of Escherichia coli developed to express the native seven-gene mevalonate pathway from Saccharomyces cerevisiae (Martin et al. 2003). This substantial improvement was made by varying only a single component of the pathway (HMG-CoA reductase) and subsequent host optimization to improve cofactor availability. We characterized and tested five variant HMG-CoA reductases obtained from publicly available genome databases with differing kinetic properties and cofactor requirements. The results of our in vitro and in vivo analyses of these enzymes implicate substrate inhibition of mevalonate kinase as an important factor in optimization of the engineered mevalonate pathway. Consequently, the NADH-dependent HMG-CoA reductase from Delftia acidovorans, which appeared to have the optimal kinetic parameters to balance HMG-CoA levels below the cellular toxicity threshold of E. coli and those of mevalonate below inhibitory concentrations for mevalonate kinase, was identified as the best producer for amorphadiene (54% improvement over the native pathway enzyme, resulting in 2.5mM or 520 mg/L of amorphadiene after 48 h). We further enhanced performance of the strain bearing the D. acidovorans HMG-CoA reductase by increasing the intracellular levels of its preferred cofactor (NADH) using a NAD(+)-dependent formate dehydrogenase from Candida boidinii, along with formate supplementation. This resulted in an overall improvement of the system by 120% resulting in 3.5mM or 700 mg/L amorphadiene after 48 h of fermentation. This comprehensive study incorporated analysis of several key parameters for metabolic design such as in vitro and in vivo kinetic performance of variant enzymes, intracellular levels of protein expression, in-pathway substrate inhibition and cofactor management to enable the observed improvements. These metrics may be applied to a broad range of heterologous pathways for improving the production of biologically derived compounds.

Clinical features, diagnostics, and outcomes of patients presenting with acute respiratory illness: a comparison of patients with and without COVID-19.

BACKGROUND: Emerging data on the clinical presentation, diagnostics, and outcomes of patients with COVID-19 have largely been presented as case series. Few studies have compared these clinical features and outcomes of COVID-19 to other acute respiratory illnesses. METHODS: We examined all patients presenting to an emergency department in San Francisco, California between February 3 and March 31, 2020 with an acute respiratory illness who were tested for SARS-CoV-2. We determined COVID-19 status by PCR and metagenomic next generation sequencing (mNGS). We compared demographics, comorbidities, symptoms, vital signs, and laboratory results including viral diagnostics using PCR and mNGS. Among those hospitalized, we determined differences in treatment (antibiotics, antivirals, respiratory support) and outcomes (ICU admission, ICU interventions, acute respiratory distress syndrome, cardiac injury). FINDINGS: In a cohort of 316 patients, 33 (10%) tested positive for SARS-CoV-2; 31 patients, all without COVID-19, tested positive for another respiratory virus (16%). Among patients with additional viral testing, no co-infections with SARS-CoV-2 were identified by PCR or mNGS. Patients with COVID-19 reported longer symptoms duration (median 7 vs. 3 days), and were more likely to report fever (82% vs. 44%), fatigue (85% vs. 50%), and myalgias (61% vs 27%); p<0.001 for all comparisons. Lymphopenia (55% vs 34%, p=0.018) and bilateral opacities on initial chest radiograph (55% vs. 24%, p=0.001) were more common in patients with COVID-19. Patients with COVID-19 were more often hospitalized (79% vs. 56%, p=0.014). Of 186 hospitalized patients, patients with COVID-19 had longer hospitalizations (median 10.7d vs. 4.7d, p<0.001) and were more likely to develop ARDS (23% vs. 3%, p<0.001). Most comorbidities, home medications, signs and symptoms, vital signs, laboratory results, treatment, and outcomes did not differ by COVID-19 status. INTERPRETATION: While we found differences in clinical features of COVID-19 compared to other acute respiratory illnesses, there was significant overlap in presentation and comorbidities. Patients with COVID-19 were more likely to be admitted to the hospital, have longer hospitalizations and develop ARDS, and were unlikely to have co-existent viral infections. These findings enhance understanding of the clinical characteristics of COVID-19 in comparison to other acute respiratory illnesses. .

Genetic interaction mapping with microfluidic-based single cell sequencing.

Genetic interaction mapping is useful for understanding the molecular basis of cellular decision making, but elucidating interactions genome-wide is challenging due to the massive number of gene combinations that must be tested. Here, we demonstrate a simple approach to thoroughly map genetic interactions in bacteria using microfluidic-based single cell sequencing. Using single cell PCR in droplets, we link distinct genetic information into single DNA sequences that can be decoded by next generation sequencing. Our approach is scalable and theoretically enables the pooling of entire interaction libraries to interrogate multiple pairwise genetic interactions in a single culture. The speed, ease, and low-cost of our approach makes genetic interaction mapping viable for routine characterization, allowing the interaction network to be used as a universal read out for a variety of biology experiments, and for the elucidation of interaction networks in non-model organisms.

Abseq: Ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding.

Proteins are the primary effectors of cellular function, including cellular metabolism, structural dynamics, and information processing. However, quantitative characterization of proteins at the single-cell level is challenging due to the tiny amount of protein available. Here, we present Abseq, a method to detect and quantitate proteins in single cells at ultrahigh throughput. Like flow and mass cytometry, Abseq uses specific antibodies to detect epitopes of interest; however, unlike these methods, antibodies are labeled with sequence tags that can be read out with microfluidic barcoding and DNA sequencing. We demonstrate this novel approach by characterizing surface proteins of different cell types at the single-cell level and distinguishing between the cells by their protein expression profiles. DNA-tagged antibodies provide multiple advantages for profiling proteins in single cells, including the ability to amplify low-abundance tags to make them detectable with sequencing, to use molecular indices for quantitative results, and essentially limitless multiplexing.

Droplet barcoding for massively parallel single-molecule deep sequencing.

The ability to accurately sequence long DNA molecules is important across biology, but existing sequencers are limited in read length and accuracy. Here, we demonstrate a method to leverage short-read sequencing to obtain long and accurate reads. Using droplet microfluidics, we isolate, amplify, fragment and barcode single DNA molecules in aqueous picolitre droplets, allowing the full-length molecules to be sequenced with multi-fold coverage using short-read sequencing. We show that this approach can provide accurate sequences of up to 10 kb, allowing us to identify rare mutations below the detection limit of conventional sequencing and directly link them into haplotypes. This barcoding methodology can be a powerful tool in sequencing heterogeneous populations such as viruses.