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  1. Microtubules are dynamic cytoskeletal filaments that undergo stochastic switching between phases of polymerization and depolymerization—a behavior known as dynamic instability. Many important cellular processes, including cell motility, chromosome segregation, and intracellular transport, require complex spatiotemporal regulation of microtubule dynamics. This coordinated regulation is achieved through the interactions of numerous microtubule-associated proteins (MAPs) with microtubule ends and lattices. Here, we review the recent advances in our understanding of microtubule regulation, focusing on results arising from biochemical in vitro reconstitution approaches using purified multiprotein ensembles. We discuss how the combinatory effects of MAPs affect both the dynamics of individual microtubule ends, as well as the stability and turnover of the microtubule lattice. In addition, we highlight new results demonstrating the roles of protein condensates in microtubule regulation. Our overall intent is to showcase how lessons learned from reconstitution approaches help unravel the regulatory mechanisms at play in complex cellular environments. 
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    Free, publicly-accessible full text available December 1, 2024
  2. Abstract

    The performance of computational methods and software to identify differentially expressed features in single‐cell RNA‐sequencing (scRNA‐seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA‐seq expression features. To model the technological variability in cross‐platform scRNA‐seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA‐seq expression profiles across experimental platforms induced by platform‐ and gene‐specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero‐inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero‐inflated scRNA‐seq data with excessive zero counts. Using both synthetic and published plate‐ and droplet‐based scRNA‐seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state‐of‐the‐art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open‐source software (R/Bioconductor package) is available athttps://github.com/himelmallick/Tweedieverse.

     
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