Biology subjects

Browning, V. E.

Publications and source records attributed to Browning, V. E..

3 recordsLinked to original sources

bioRxiv2025

Capsule-Based Single-Cell Genome Sequencing

Single-cell genomics methods have unveiled the heterogeneity present in seemingly homogenous populations of cells, however, these techniques require meticulous optimization. How exactly does one handle and manipulate the biological contents from a single cell? Here, we introduce and characterize a novel semi-permeable capsule (SPC), capable of isolating single cells and their contents while facilitating biomolecular exchange based on size-selectivity. These capsules maintain stability under diverse physical and chemical conditions and allow selective diffusion of biomolecules, effectively retaining larger biomolecules including genomic DNA, and cellular complexes, while permitting the exchange of smaller molecules, including primers and enzymes. We demonstrate the utility of SPCs for single cell assays by performing the simultaneous culture of over 500,000 cellular colonies, demonstrating efficient and unbiased nucleic acid amplification, and performing combinatorial indexing-based single-cell whole genome sequencing (sc-WGS). Notably, SPC-based sc-WGS facilitates uniform genome coverage and minimal cross-contamination allowing for the detection of genomic variants with high sensitivity and specificity. Leveraging these properties, we conducted a proof-of-concept lineage tracing experiment using cells harboring the hypermutator polymerase {varepsilon} allele (POLE P286R). Sequencing of 1000 single cell genomes at low depth facilitated the capture of lineage marks deposited throughout the genome during each cell division and the subsequent reconstruction of cellular genealogies. Capsule-based sc-WGS expands the single-cell genomics toolkit and will facilitate the investigation of somatic variants, resolved to single cells at scale.

genomics↗

bioRxiv2024

Predicting cell cycle stage from 3D single-cell nuclear-stained images

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models--a support vector machine (SVM) and a deep neural network--to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08-0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.

cell biology↗

bioRxiv2023

Programmable peroxidase-assisted signal amplification enables flexible detection of nucleic acid targets in cellular and histopathological specimens

In situ hybridization (ISH) is a powerful tool for investigating the spatial arrangement of nucleic acid targets in fixed samples. ISH is typically visualized using fluorophores to allow high sensitivity and multiplexing or with colorimetric labels to facilitate co-visualization with histopathological stains. Both approaches benefit from signal amplification, which makes target detection effective, rapid, and compatible with a broad range of optical systems. Here, we introduce a unified technical platform, termed pSABER, for the amplification of ISH signals in cell and tissue systems. pSABER decorates the in situ target with concatemeric binding sites for a horseradish peroxidase-conjugated oligonucleotide which can then catalyze the massive localized deposition of fluorescent or colorimetric substrates. We demonstrate that pSABER effectively labels DNA and RNA targets, works robustly in cultured cells and challenging formalin fixed paraffin embedded (FFPE) specimens. Furthermore, pSABER can achieve 25-fold signal amplification over conventional signal amplification by exchange reaction (SABER) and can be serially multiplexed using solution exchange. Therefore, by linking nucleic acid detection to robust signal amplification capable of diverse readouts, pSABER will have broad utility in research and clinical settings.

genomics↗