More than 50,000 single-cell genomes are sequenced in a single run using droplet barcoding. The application of single-cell genome sequencing to large cell populations has been hindered by technical challenges in isolating single cells during genome preparation. Here we present single-cell genomic sequencing (SiC-seq), which uses droplet microfluidics to isolate, fragment, and barcode the genomes of single cells, followed by Illumina sequencing of pooled DNA. We demonstrate ultra-high-throughput sequencing of >50,000 cells per run in a synthetic community of Gram-negative and Gram-positive bacteria and fungi. The sequenced genomes can be sorted in silico based on characteristic sequences. We use this approach to analyze the distributions of antibiotic-resistance genes, virulence factors, and phage sequences in microbial communities from an environmental sample. The ability to routinely sequence large populations of single cells will enable the de-convolution of genetic heterogeneity in diverse cell populations.

We describe a simple bulk method to prepare double emulsions for high-throughput analysis and sorting with flow cytometers.

The spatial organization of molecules in a cell is essential for performing their functions. Spatial transcriptomics technologies have opened the door to characterization of cellular and subcellular organization. While current computational methods focus on discerning tissue architecture, cell-cell interactions and spatial expression patterns, these approaches are limited to investigating spatial variation at the multicellular scale. We present Bento, a Python toolkit that fully takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three fundamental analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. To demonstrate the toolkit, we apply these methods to a variety of datasets including U2-OS cells (MERFISH), 3T3 cells (seqFISH+), and treated cardiomyocytes (Molecular Cartography). We quantify RNA localization changes in cardiomyocytes identifying mRNA depletion of critical cardiac disease-associated genes RBM20 and CACNB2 from the endoplasmic reticulum upon doxorubicin treatment. The Bento package is a member of the open-source Scverse ecosystem, enabling integration with other single-cell omics analysis tools.

Microfluidic devices are valuable for a variety of biotechnology applications, such as synthesizing biochemical libraries, screening enzymes, and analyzing single cells. However, normally, the devices are controlled using specialized pumps, which require expert knowledge to operate. Here, we demonstrate operation of poly(dimethylsiloxane) devices without pumps. We build a scaffold that holds the device and reagents to be infused in a format that can be inserted into a 50 mL falcon tube and spun in a common lab centrifuge. By controlling the device design and centrifuge spin speed, we infuse the reagents at controlled flow rates. We demonstrate the encapsulation and culture of clonal colonies of red and green Escherichia coli in droplets seeded from single cells.

Computational sequence-based predictors of protein localization have the potential to accelerate the discovery of protein functions and interactions, thereby advancing our understanding of human biology and disease. While many methods have been proposed, evaluations remain limited by small test sets, coarse-grained cellular compartment labels and single-label classification, despite the fact that nearly half of human proteins localize to multiple compartments. Here we integrate annotations from major protein databases to construct a highly validated, twofold larger benchmark test set of 3,814 human proteins. Using this dataset, we systematically evaluate existing sequence-based predictors and compare combinations of protein language models and aggregation strategies. We find that current models underperform on fine-grained compartments, multilocalizing proteins and pathogenic variants known to mislocalize. Our results reveal fundamental limitations of existing approaches and underscore the need for improved models, standardized benchmark datasets and more rigorous evaluation in subcellular localization prediction.