Highlights d MYC overexpression reversibly induces CIN by reprogramming mitotic gene expression d MYC impairs mitotic spindle formation d High TPX2 expression allows cells that overexpress MYC to adapt to spindle stress d TPX2 depletion is synthetic lethal with MYC overexpression

Graphical Abstract Highlights d Mammalian spindles use microtubule end-clustering by dynein or NuMA to hold their shape d Dynein or NuMA knockout spindles are unstable and turbulent d The kinesin Eg5 expands turbulent spindle networks and drives shape change d Turbulent spindles reorganize cytoplasm and increase cell movement

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Chylous pleural effusion (chylothorax) frequently accompanies lymphatic vessel malformations and other conditions with lymphatic defects. Although retrograde flow of chyle from the thoracic duct is considered a potential mechanism underlying chylothorax in patients and mouse models, the path chyle takes to reach the thoracic cavity is unclear. Herein, we use a novel transgenic mouse model, where doxycycline-induced overexpression of vascular endothelial growth factor (VEGF)-C was driven by the adipocyte-specific promoter adiponectin (ADN), to determine how chylothorax forms. Surprisingly, 100% of adult ADN-VEGF-C mice developed chylothorax within 7 days. Rapid, consistent appearance of chylothorax enabled us to examine the step-by-step development in otherwise normal adult mice. Dynamic imaging with a fluorescent tracer revealed that lymph in the thoracic duct of these mice could enter the thoracic cavity by retrograde flow into enlarged paravertebral lymphatics and subpleural lymphatic plexuses that had incompetent lymphatic valves. Pleural mesothelium overlying the lymphatic plexuses underwent exfoliation that increased during doxycycline exposure. Together, the findings indicate that chylothorax in ADN-VEGF-C mice results from retrograde flow of chyle from the thoracic duct into lymphatic tributaries with defective valves. Chyle extravasates from these plexuses and enters the thoracic cavity through exfoliated regions of the pleural mesothelium.

Solid organ transplant remains a life-saving therapy for children with end-stage heart, lung, liver, or kidney disease; however, ~25% of allograft recipients experience acute rejection within the first 12 months after transplant. Our ability to detect rejection early and to develop less toxic immunosuppressive agents is hampered by an incomplete understanding of the immune changes associated with rejection, particularly in the pediatric population. Here we used high-dimensional single-cell proteomic technologies (CyTOF) to generate the first detailed, multi-lineage analysis of the peripheral blood immune composition of pediatric solid organ transplant recipients. We report that the organ transplanted impacts the immune composition post-transplant. When taking these allograft-specific differences into account, we further observed that differences in the proportion of subsets of CD8 and CD4 T cells were significantly associated with allograft health. Together, these data form the basis for mechanistic studies into the pathobiology of rejection to develop less invasive tools to identify early rejection and new immunosuppressive agents with greater specificity and less toxicity.

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Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human understanding of data and are useful for guiding both quantitative and qualitative analysis of cellular relationships. Existing algorithms are typically unsupervised, utilizing only measured features to generate manifolds, disregarding known biological labels such as cell type or experimental timepoint. Here, we repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling users to tailor visualizations to separate specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this flexible, computationally-efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes, such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality reduction algorithms and illustrate its utility and versatility for exploration of single-cell mass cytometry, transcriptomics and chromatin accessibility data.

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