Microbes frequently live in nature as small, densely packed aggregates containing ∼10(1)-10(5) cells. These aggregates not only display distinct phenotypes, including resistance to antibiotics, but also, serve as building blocks for larger biofilm communities. Aggregates within these larger communities display nonrandom spatial organization, and recent evidence indicates that this spatial organization is critical for fitness. Studying single aggregates as well as spatially organized aggregates remains challenging because of the technical difficulties associated with manipulating small populations. Micro-3D printing is a lithographic technique capable of creating aggregates in situ by printing protein-based walls around individual cells or small populations. This 3D-printing strategy can organize bacteria in complex arrangements to investigate how spatial and environmental parameters influence social behaviors. Here, we combined micro-3D printing and scanning electrochemical microscopy (SECM) to probe quorum sensing (QS)-mediated communication in the bacterium Pseudomonas aeruginosa. Our results reveal that QS-dependent behaviors are observed within aggregates as small as 500 cells; however, aggregates larger than 2,000 bacteria are required to stimulate QS in neighboring aggregates positioned 8 μm away. These studies provide a powerful system to analyze the impact of spatial organization and aggregate size on microbial behaviors.
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http://dx.doi.org/10.1073/pnas.1421211111 | DOI Listing |
Nat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFGlob Chang Biol
January 2025
School of Biological Sciences, The University of Hong Kong, Hong Kong, China.
Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α-diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e.
View Article and Find Full Text PDFZoological Lett
January 2025
National Institutes of Natural Sciences, Exploratory Research Center On Life and Living Systems (ExCELLS), National Institute for Basic Biology, Okazaki, Aichi, 444-8787, Japan.
In vertebrates, skeletal muscle comprises fast and slow fibers. Slow and fast muscle cells in fish are spatially segregated; slow muscle cells are located only in a superficial region, and comprise a small fraction of the total muscle cell mass. Slow muscles support low-speed, low-force movements, while fast muscles are responsible for high-speed, high-force movements.
View Article and Find Full Text PDFGenome Biol
January 2025
Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, 90095, CA, USA.
Deciphering the link between tissue architecture and function requires methods to identify and interpret patterns in spatial arrangement of cells. We present SMORE, an approach to detect patterns in sequential arrangements of cells and examine their associated gene expression specializations. Applied to retina, brain, and embryonic tissue maps, SMORE identifies novel spatial motifs, including one that offers a new mechanism of action for type 1b bipolar cells.
View Article and Find Full Text PDFNat Commun
January 2025
Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package.
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