Biology subjects

Munoz, A. F.

Publications and source records attributed to Munoz, A. F..

3 recordsLinked to original sources

bioRxiv2025

Alternate dyes for image-based profiling assays

BackgroundCell Painting, the leading image-based profiling assay, involves staining plated cells with six dyes that mark the different compartments in a cell. Such profiles can then be used to discover connections between samples (whether different cell lines, different genetic treatments, or different compound treatments) as well as to assess particular features impacted by each treatment. Researchers may wish to vary the standard dye panel to assess particular phenotypes, or image cells live while maintaining the ability to cluster profiles overall. MethodsIn this study, we evaluate the performance of dyes that can either replace or augment the traditional Cell Painting dyes or enable tracking live cell dynamics. We perturbed U2OS cells with 90 different compounds and subsequently stained them with either standard Cell Painting dyes (Revvity), or with MitoBrilliant (Tocris) replacing MitoTracker or Phenovue phalloidin 400LS (Revvity) replacing phalloidin. We also tested the live-cell compatible ChromaLive dye (Saguaro). ResultsAll dye sets effectively separated biological replicates of the same sample vs. negative controls (phenotypic activity), although separating from replicates of all other compounds (phenotypic distinctiveness) proved challenging for all dye sets. While individual dye substitutions within the standard Cell Painting panel had minimal impact on assay performance, the live cell dye exhibited distinct performance profiles across different compound classes compared to the standard panel, with later time points more distinct than earlier ones. DiscussionSubstituting MitoBrilliant or Phenovue phalloidin 400LS for standard mitochondrial or actin dyes minimally impacted Cell Painting assay performance. Phenovue phalloidin 400LS offers the advantage of isolating actin features from Golgi or plasma membrane while accommodating an additional 568nm dye. Live cell imaging, enabled by ChromaLive dye, provides real-time assessment of compound-induced morphological changes. Combining this with the standard Cell Painting assay significantly expands the feature space for enhanced cellular profiling. Our findings provide data-driven options for researchers selecting dye sets for image-based profiling.

cell biology↗

bioRxiv2024

Morphological map of under- and over-expression of genes in human cells

Cell Painting images offer valuable insights into a cells state and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP (Joint Undertaking in Morphological Profiling) Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features. These profiles capture the phenotypic impacts of perturbing 15,243 human genes, including overexpressing 12,609 genes (using open reading frames, ORFs) and knocking out 7,975 genes (using CRISPR-Cas9). We mitigated technical artifacts by rigorously evaluating data processing options and validated the datasets robustness and biological relevance. Analysis of phenotypic profiles revealed novel gene clusters and functional relationships, including those associated with mitochondrial function, cancer, and neural processes. The JUMP Cell Painting genetic dataset is a valuable resource for exploring gene relationships and uncovering novel functions.

bioinformatics↗

bioRxiv2022

A label-free method to track individuals and lineages of budding cells

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging because cells often overlap in images, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae. Here we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages, estimates growth rates as the rate of change of volumes, and identifies cytokinesis by how growth varies. Using BABY and a microfluidic device, we show that bud growth is first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. Growth rate and fitness are strongly correlated, and BABY should therefore generate much biological insight.

systems biology↗