The evolution of large organismal size is fundamentally important for multicellularity, creating new ecological niches and opportunities for the evolution of increased biological complexity. Yet little is known about how large size evolves, particularly in nascent multicellular organisms that lack genetically-regulated multicellular development. Here we examine the interplay between biological and biophysical drivers of macroscopic multicellularity using long-term experimental evolution. Over 600 daily transfers (~3,000 generations), multicellular snowflake yeast evolved macroscopic size, becoming ~2·104 times larger (~mm scale) and 104-fold more biophysically tough, while remaining clonal. They accomplished this through sustained biophysical adaptation, evolving increasingly elongate cells that initially reduced the strain of cellular packing, then facilitated branch entanglement so that groups of cells stay together even after many cellular bonds fracture. Four out of five replicate populations show evidence of predominantly adaptive evolution, with mutations becoming significantly enriched in genes affecting cell shape and cell-cell bonds. Taken together, this work shows how selection acting on the emergent properties of simple multicellular groups can drive sustained biophysical adaptation, an early step in the evolution of increasingly complex multicellular organisms.
Neural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure. Background and SummaryWhether focusing on a large swath of cortex or a single subcortical nucleus, consistent and reliable visualization of cytoarchitecture is critical for the creation of reference points which demarcate the brain's landscape [1]. This is true not only for the identification of landmarks (or regions of interest), but also the study of local circuits therein. Indeed, it is the distinguishing features in brain cytoarchitecture which arise at small, local scales (i.e., through clusters of cells which are packed in discrete barrels or layers; see [2,3]) that continue to emerge across larger spatial scales to reveal the presence of functionally distinct regions. Thus, detailed views into the brain's architecture can be used to experimentally manipulate circuits, and to advance the field's understanding and integration of each of these overarching systems.With advances in the reconstruction and analysis of significantly larger brain volumes, neuroscientists are now able to visualize patterns of microarchitecture that arise at a scale previously inaccessible using traditional methods [4,5,6]. Examples such as CLARITY [7], expansion microscopy [8], serial two photon tomography [9, 10, 11], multi-beam scanning electron microscopy [12], and X-ray microtomography [13,14,15,16,17], now provide access to several regions of interest within a volume of tissue simultaneously, providing rich context to study both local circuitry and long-range projections. With many of these new techniques, it is possible to image and analyze large intact anatomical samples that preserve the connectivity between multiple regions of interest [18,19], thus providing a lens into the heterogeneity of neural structure within and across different brain areas.Here, we introduce a three-dimensional neuroanatomical dataset extracted from a validated, in-vitro mouse thalamocortical sample spanning six anatomically distinct regions of interest (somatosensory cortex, two thalamic nuclei, zona incerta, striatum and hypothalamus) [18]. This dataset was reconstructed using X-ray microtomography to reveal a diverse composition of microstructures (e.g., myelinated axons, cell bodies, and vasculature) within each region at isotr...
Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in “batches.” Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (~40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 ± 110 μm (1 s.d.), y-direction: 60 ± 150 μm (1 s.d.)) with a mean cycle time of 43 s ± 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.