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Biology subjects

McGeary, S. E.

Publications and source records attributed to McGeary, S. E..

5 recordsLinked to original sources

Tissue Fluidity: A Double-Edged Sword for Multicellular Patterning

The organization of cells into spatial patterns is a fundamental aspect of multicellularity. One major mechanism underlying tissue patterning is adhesion-based cell sorting, in which a heterogeneous mixture of cell types spontaneously separates into distinct domains based on differences in adhesion protein expression. Here, we identify tissue fluidity--the extent to which cells can move freely within a tissue--as a critical regulator of adhesion-based sorting. First, we describe a physically well-understood minimal tissue model that can integrate both tissue fluidity and adhesion-based sorting, and demonstrate that this model can quantitatively reproduce experimentally measured sorting dynamics in a fibroblast cell culture assay. We go on to show that altering tissue fluidity by any mechanism in the model leads to substantial changes in the rate or accuracy of sorting (or both). We further demonstrate that the balance between cell motility, which acts to fluidize the tissue, and homotypic cell-cell adhesion, which acts to solidify the tissue, sensitively tunes a fundamental trade-off between the rate and accuracy of sorting--such that sorting can only occur when motility and adhesion are tightly coupled. Intriguingly, best fits of the simulations to the experiments across a range of adhesion protein expression conditions suggest that cells may naturally scale their motility strength with their adhesion strength - thereby maintaining a permissive fluidity for sorting. Overall, our results indicate that tissue fluidity must be tightly regulated for sorting to occur, and that cells may have evolved a mechanism to naturally co-regulate their mechanical properties in order to sustain a patterning-competent fluidity. Statement of SignificanceTissue fluidity, or the ability of cells to freely rearrange within a tissue, is a universal property of multicellular organisms that plays central roles in development, cancer, and wound healing. Here, we identify tissue fluidity as a critical regulator of a major mechanism of multicellular patterning - adhesion-based cell sorting. The results of our combined experimental-computational investigation suggest that tissues can readily tune their fluidity in order to freeze, catalyze, or erase multicellular patterns - carrying significant implications for our understanding of how patterns are formed in development, lost in diseases affecting tissue organization (e.g., cancer), regained through the processes of wound healing and regeneration, and can be engineered in the creation of synthetic organoid and embryoid systems. One sentence summaryBiophysical modeling demonstrates how tissue fluidity is a key regulator of the rate and accuracy of adhesion-based sorting.

cell biology↗

A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells

Cellular lineage histories along with their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have limited barcode diversity and poor single-cell lineage coverage, thus precluding their use in tissues composed of millions of cells. Here, we developed DARLIN, an improved Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) to enhance insertion events over 30 CRISPR target sites, stably integrated into 3 distinct genomic loci. DARLIN is inducible, has an estimated ~1018 lineage barcodes across tissues, and enables detection of usable barcodes in ~60% of profiled single cells. Using DARLIN, we examined fate priming within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we adapted a method to jointly profile DNA methylation, chromatin accessibility, gene expression, and lineage information in single cells. DARLIN will enable widespread high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.

developmental biology↗

Pairing to the microRNA 3' region occurs through two alternative binding modes, with affinity shaped by nucleotide identity as well as pairing position

MicroRNAs (miRNAs), in association with Argonaute (AGO) proteins, direct repression by pairing to sites within mRNAs. Compared to pairing preferences of the miRNA seed region (nucleotides 2-8), preferences of the miRNA 3' region are poorly understood, due to the sparsity of measured affinities for the many pairing possibilities. We used RNA bind-n-seq with purified AGO2-miRNA complexes to measure relative affinities of >1,000 3'-pairing architectures for each miRNA. In some cases, optimal 3' pairing increased affinity by >500-fold. Some miRNAs had two high-affinity 3'-pairing modes--one of which included additional nucleotides bridging seed and 3' pairing to enable high-affinity pairing to miRNA nucleotide 11. The affinity of binding and the position of optimal pairing both tracked with the occurrence of G or oligo(G/C) nucleotides within the miRNA. These and other results advance understanding of miRNA targeting, providing insight into how optimal 3' pairing is determined for each miRNA. HIGHLIGHTSO_LIRNA bind-n-seq reveals relative affinities of >1,000 3'-pairing architectures C_LIO_LITwo distinct 3'-binding modes can enhance affinity, by >500-fold in some instances C_LIO_LIG and oligo(G/C) residues help define the miRNA 3' segment most critical for pairing C_LIO_LISeed mismatch identity can influence the contribution of compensatory 3' pairing C_LI

biochemistry↗

The Dynamics of Cytoplasmic mRNA Metabolism

For all but a few mRNAs, the dynamics of metabolism are unknown. Here, we developed an experimental and analytical framework for examining these dynamics for mRNAs from thousands of genes. mRNAs of mouse fibroblasts exit the nucleus with diverse intragenic and intergenic poly(A)-tail lengths. Once in the cytoplasm, they have a broad (1000-fold) range of deadenylation rate constants, which correspond to cytoplasmic lifetimes. Indeed, with few exceptions, degradation appears to occur primarily through deadenylation-linked mechanisms, with little contribution from either endonucleolytic cleavage or deadenylation-independent decapping. Most mRNA molecules degrade only after their tail lengths fall below 25 nt. Decay rate constants of short-tailed mRNAs vary broadly (1000-fold) and are more rapid for short-tailed mRNAs that had previously undergone more rapid deadenylation. This coupling helps clear rapidly deadenylated mRNAs, enabling the large range in deadenylation rate constants to impart a similarly large range in stabilities.\n\nHighlightsO_LImRNAs enter the cytoplasm with diverse intra- and intergenic lengths\nC_LIO_LImRNA deadenylation rates span a 1000-fold range and correspond to mRNA half-lives\nC_LIO_LIAfter their tails become short, mRNAs decay at rates that span a 1000-fold range\nC_LIO_LIMore rapidly deadenylated mRNAs decay more rapidly upon reaching short tail lengths\nC_LI

genomics↗

The biochemical basis of microRNA targeting efficacy

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all [≤]12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

biochemistry↗