Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution.

We designed an epi-illumination SPIM system that uses a single objective and has a sample interface identical to that of an inverted fluorescence microscope with no additional reflection elements. It achieves subcellular resolution and single-molecule sensitivity, and is compatible with common biological sample holders, including multi-well plates. We demonstrated multicolor fast volumetric imaging, single-molecule localization microscopy, parallel imaging of 16 cell lines and parallel recording of cellular responses to perturbations.

Torques within and outside the human spindle balance twist at anaphase.

At each cell division, nanometer-scale motors and microtubules give rise to the micron-scale spindle. Many mitotic motors step helically around microtubules in vitro, and most are predicted to twist the spindle in a left-handed direction. However, the human spindle exhibits only slight global twist, raising the question of how these molecular torques are balanced. Here, we find that anaphase spindles in the epithelial cell line MCF10A have a high baseline twist, and we identify factors that both increase and decrease this twist. The midzone motors KIF4A and MKLP1 are together required for left-handed twist at anaphase, and we show that KIF4A generates left-handed torque in vitro. The actin cytoskeleton also contributes to left-handed twist, but dynein and its cortical recruitment factor LGN counteract it. Together, our work demonstrates that force generators regulate twist in opposite directions from both within and outside the spindle, preventing strong spindle twist during chromosome segregation.

Kinetochore-fiber lengths are maintained locally but coordinated globally by poles in the mammalian spindle.

At each cell division, nanometer-scale components self-organize to build a micron-scale spindle. In mammalian spindles, microtubule bundles called kinetochore-fibers attach to chromosomes and focus into spindle poles. Despite evidence suggesting that poles can set spindle length, their role remains poorly understood. In fact, many species do not have spindle poles. Here, we probe the pole's contribution to mammalian spindle length, dynamics, and function by inhibiting dynein to generate spindles whose kinetochore-fibers do not focus into poles, yet maintain a metaphase steady-state length. We find that unfocused kinetochore-fibers have a mean length indistinguishable from control, but a broader length distribution, and reduced length coordination between sisters and neighbors. Further, we show that unfocused kinetochore-fibers, like control, can grow back to their steady-state length if acutely shortened by drug treatment or laser ablation: they recover their length by tuning their end dynamics, albeit slower due to their reduced baseline dynamics. Thus, kinetochore-fiber dynamics are regulated by their length, not just pole-focusing forces. Finally, we show that spindles with unfocused kinetochore-fibers can segregate chromosomes but fail to correctly do so. We propose that mammalian spindle length emerges locally from individual k-fibers while spindle poles globally coordinate k-fibers across space and time.

Torques within and outside the human spindle balance twist at anaphase.

At each cell division, nanometer-scale motors and microtubules give rise to the micron-scale spindle. Many mitotic motors step helically around microtubules in vitro, and most are predicted to twist the spindle in a left-handed direction. However, the human spindle exhibits only slight global twist, raising the question of how these molecular torques are balanced. Here, using lattice light sheet microscopy, we find that anaphase spindles in the epithelial cell line MCF10A have a high baseline twist, and we identify factors that both increase and decrease this twist. The midzone motors KIF4A and MKLP1 are redundantly required for left-handed twist at anaphase, and we show that KIF4A generates left-handed torque in vitro. The actin cytoskeleton also contributes to left-handed twist, but dynein and its cortical recruitment factor LGN counteract it. Together, our work demonstrates that force generators regulate twist in opposite directions from both within and outside the spindle, preventing strong spindle twist during chromosome segregation.

OpenCell: Endogenous tagging for the cartography of human cellular organization.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

Opposing motors provide mechanical and functional robustness in the human spindle.

At each cell division, the spindle self-organizes from microtubules and motors. In human spindles, the motors dynein and Eg5 generate contractile and extensile stress, respectively. Inhibiting dynein or its targeting factor NuMA leads to unfocused, turbulent spindles, and inhibiting Eg5 leads to monopoles; yet, bipolar spindles form when both are inhibited together. What, then, are the roles of these opposing motors? Here, we generate NuMA/dynein- and Eg5-doubly inhibited spindles that not only attain a typical metaphase shape and size but also undergo anaphase. However, these spindles have reduced microtubule dynamics and are mechanically fragile, fracturing under force. Furthermore, they exhibit lagging chromosomes and a dramatic left-handed twist at anaphase. Thus, although these opposing motors are not required for spindle shape, they are essential to its mechanical and functional robustness. This work suggests a design principle whereby opposing active stresses provide robustness to force-generating cellular structures.

Revealing architectural order with quantitative label-free imaging and deep learning.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within. Often, fluorescent dyes or trackers are used that can be detected under the microscope. However, this procedure can sometimes interfere with the biological processes being studied. Now, Guo, Yeh, Folkesson et al. have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label. The technique, called QLIPP, uses the phase and polarization of the light passing through the sample to get information about its makeup. A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human. This way, Guo et al. were able to reveal details that conventional microscopy would have missed. Then, a type of machine learning, known as 'deep learning', was used to translate the density and orientation images into fluorescence images, which enabled the researchers to predict specific structures in human brain tissue sections. QLIPP can be added as a module to a microscope and its software is available open source. Guo et al. hope that this approach can be used across many fields of biology, for example, to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection. However, further work in automating other aspects, such as sample preparation and analysis, will be needed to realize the full benefits.