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Patterned surfaces can enhance the sensitivity of laser desorption ionization mass spectrometry by segregating and concentrating analytes, but their fabrication can be challenging. Here, a simple method to fabricate substrates patterned with micrometer-scale wells that yield more accurate and sensitive mass spectrometry measurements compared to flat surfaces is described. The wells can also concentrate and localize cells and beads for cell-based assays.

Droplet digital PCR provides superior accuracy for nucleic acid quantitation. The requirement of microfluidics to generate and analyze the emulsions, however, is a barrier to its adoption, particularly in low resource settings or clinical laboratories. Here, we report a novel method to prepare ddPCR droplets by vortexing and readout of the results by bulk analysis of recovered amplicons. We demonstrate the approach by accurately quantitating SARS-CoV-2 sequences using entirely bulk processing and no microfluidics. Our approach for quantitating reactions should extend to all digital assays that generate amplicons, including digital PCR and LAMP conducted in droplets, microchambers, or nanoliter wells. More broadly, our approach combines important attributes of ddPCR, including enhanced accuracy and robustness to inhibition, with the high-volume sample processing ability of quantitative PCR.

Targeted sequencing enables sensitive and cost-effective analysis by focusing resources on molecules of interest. Existing methods, however, are limited in enrichment power and target capture length. Here, we present a novel method that uses compound nucleic acid cytometry to achieve million-fold enrichments of molecules >10 kbp in length using minimal prior target information. We demonstrate the approach by sequencing HIV proviruses in infected individuals. Our method is useful for rare target sequencing in research and clinical applications, including for identifying cancer-associated mutations or sequencing viruses infecting cells.

Dispensing micron-scale droplets from a suspended nozzle is important for applications in bioprinting, analytical chemistry, and pharmaceutical formulation. Here, we describe a general approach to eject droplets from microfluidic devices using superhydrophobic patterning; this facilitates release of wetted fluids, allowing droplets to break contact with channel surfaces and travel along regular paths to achieve a printing accuracy of ∼3 μm. We demonstrate the utility of the approach by using it to print droplets of varied composition from a microfluidic mixing device. Our approach is compatible with common fabrication techniques making it applicable to devices configured for diverse applications.

Droplet libraries consisting of many reagents encapsulated in separate droplets are necessary for applications of microfluidics, including combinatorial chemical synthesis, DNA-encoded libraries, and massively multiplexed PCR. However, existing approaches for generating them are laborious and impractical. Here, we describe an automated approach using a commercial array spotter. The approach can controllably emulsify hundreds of different reagents in a fraction of the time of manual operation of a microfluidic device, and without any user intervention. We demonstrate that the droplets produced by the spotter are similarly uniform to those produced by microfluidics and automate the generation of a ~ 2 mL emulsion containing 192 different reagents in ~ 4 h. The ease with which it can generate high diversity droplet libraries should make combinatorial applications more feasible in droplet microfluidics. Moreover, the instrument serves as an automated droplet generator, allowing execution of droplet reactions without microfluidic expertise.

Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis.

Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis.

In this study, electrical measurements on ferroelectric random-access memory by prior x-ray irradiation are conducted. Compared with an unirradiated device, parameters such as current leakage and remnant polarization of the irradiated device were unexpectedly improved. Besides, better reliabilities including the number of endurance times and retention time have also been demonstrated. To clarify the underlying physical mechanism, the electrical properties are analyzed. The current–voltage curve (I–V) implies a change in the grain size in the ferroelectric layer (FL), and the capacitance–voltage curve (C – V) profile indicates that the FL undergoes a phase change during irradiation. Finally, according to the electrical results, a physical model is proposed as an explanation. [ABSTRACT FROM AUTHOR]