Forces in a biological context

Theoretical modelling of the microtubule-Dam1-ring force generation mechanism and the pulling of tubes from surface-supported lipid bilayers are presented and discussed. Atomic force microscopy (AFM) force data of tube pulling experiments is analysed and compared with theoretical predictions. Featurescommonto recent computational models are simplified and examined independently where possible. In particular, the steric confinement of the Dam1 ring on a microtubule (MT) by protofilaments (PFs), the powerstroke produced by curling PFs, the depolymerisation of the MT, and the binding attraction between Dam1 and the MT are modelled. Model parameters are fitted to data. Functional force generation is equally demonstrated when attachment is maintained by steric confinement alone (protofilament model) or by a binding attraction alone (binding model). Moreover, parameters amenable to experimental modification are shown to induce differences between the protofilament model and the binding model. Changing the depolymerisation rate of MTs, the diffusion coefficient of the Dam1 ring, or applying an oscillating load force will allow discrimination of these two different mechanisms of force generation and kinetochore attachment. A previously described theoretical model of pulling lipid bilayer tubes from vesicles is modified for the case of pulling tubes from surface-supported lipid bilayers. A shape equation for axisymmetric membranes is derived variationally and solved numerically for zero pressure. Free energy profiles and force curves are calculated for various AFM probe sizes and compared to experimental data where a ground flat AFM probe is used to pull tubes from surface-supported lipid bilayers. The predicted force curves partially fit the experimental data, although not at short distances, and estimates of the bilayer surface tension are given. Pressure and volume profiles are calculated for the extension of the model to the nonzero pressure case

A stochastic model dissects cell states in biological transition processes

Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations

KiT : a MATLAB package for kinetochore tracking

Summary: During mitosis, chromosomes are attached to the mitotic spindle via large protein complexes called kinetochores. The motion of kinetochores throughout mitosis is intricate and automated quantitative tracking of their motion has already revealed many surprising facets of their behaviour. Here, we present ‘KiT’ (Kinetochore Tracking)—an easy-to-use, open-source software package for tracking kinetochores from live-cell fluorescent movies. KiT supports 2D, 3D and multi-colour movies, quantification of fluorescence, integrated deconvolution, parallel execution and multiple algorithms for particle localization

Modest increase of KIF11 exposes fragilities in the mitotic spindle causing chromosomal instability

Chromosomal instability (CIN), the process of increased chromosomal alterations, compromises genomic integrity and has profound consequences on human health. Yet, our understanding of the molecular and mechanistic basis of CIN initiation remains limited. We developed a high-throughput, single-cell image-based pipeline employing deep learning and spot counting models to detect CIN by automatically counting chromosomes and micronuclei. To identify CIN-initiating conditions, we used CRISPR activation in human diploid cells to upregulate, at physiologically-relevant levels, 14 genes that are functionally important in cancer. We found that upregulation of CCND1, FOXA1, and NEK2 resulted in pronounced changes in chromosome counts and KIF11 upregulation resulted in micronuclei formation. We identified KIF11-dependent fragilities within the mitotic spindle; increased KIF11 causes centrosome fragmentation, higher microtubule stability, lagging chromosomes or mitotic catastrophe. Our findings demonstrate that even modest average single gene expression changes in a karyotypically stable background are sufficient for initiating CIN by exposing fragilities of the mitotic spindle which can lead to a genomically-diverse cell population

Probing microtubule polymerisation state at single kinetochores during metaphase chromosome motion

Kinetochores regulate the dynamics of attached microtubule bundles (kinetochore-fibres, K-fibres) to generate the forces necessary for chromosome movements in mitosis. Current models suggest that poleward-moving kinetochores are attached to depolymerising K-fibres and anti-poleward-moving kinetochores to polymerising K-fibres. How the dynamics of individual microtubules within the K-fibre relate to poleward and anti-poleward movements is poorly understood. To investigate this, we developed a live-cell imaging assay combined with computational image analysis that allows eGFP-tagged EB3 (also known as MAPRE3) to be quantified at thousands of individual metaphase kinetochores as they undergo poleward and anti-poleward motion. Surprisingly, we found that K-fibres are incoherent, containing both polymerising and depolymerising microtubules – with a small polymerisation bias for anti-poleward-moving kinetochores. K-fibres also display bursts of EB3 intensity, predominantly on anti-poleward-moving kinetochores, equivalent to more coherent polymerisation, and this was associated with more regular oscillations. The frequency of bursts and the polymerisation bias decreased upon loss of kinesin-13, whereas loss of kinesin-8 elevated polymerisation bias. Thus, kinetochores actively set the balance of microtubule polymerisation dynamics in the K-fibre while remaining largely robust to fluctuations in microtubule polymerisation

Single-cell states in the estrogen response of breast cancer cell lines

Estrogen responsive breast cancer cell lines have been extensively studied to characterize transcriptional patterns in hormone-responsive tumors. Nevertheless, due to current technological limitations, genome-wide studies have typically been limited to population averaged data. Here we obtain, for the first time, a characterization at the single-cell level of the states and expression signatures of a hormone-starved MCF-7 cell system responding to estrogen. To do so, we employ a recently proposed model that allows for dissecting single-cell states from time-course microarray data. We show that within 32 hours following stimulation, MCF-7 cells traverse, most likely, six states, with a faster early response followed by a progressive deceleration. We also derive the genome-wide transcriptional profiles of such single-cell states and their functional characterization. Our results support a scenario where estrogen promotes cell cycle progression by controlling multiple, sequential regulatory steps, whose single-cell events are here identified. © 2014 Casale et al

Force transduction by the microtubule-bound Dam1 ring

The coupling between the depolymerization of microtubules (MTs) and the motion of the Dam1 ring complex is now thought to play an important role in the generation of forces during mitosis. Our current understanding of this motion is based on a number of detailed computational models. Although these models realize possible mechanisms for force transduction, they can be extended by variation of any of a large number of poorly measured parameters and there is no clear strategy for determining how they might be distinguished experimentally. Here we seek to identify and analyze two distinct mechanisms present in the computational models. In the first, the splayed protofilaments at the end of the depolymerizing MT physically prevent the Dam1 ring from falling off the end, and in the other, an attractive binding secures the ring to the microtubule. Based on this analysis, we discuss how to distinguish between competing models that seek to explain how the Dam1 ring stays on the MT. We propose novel experimental approaches that could resolve these models for the first time, either by changing the diffusion constant of the Dam1 ring (e.g., by tethering a long polymer to it) or by using a time-varying load

Average force decomposition across the population of trajectories.

<p>(A) Distributions of absolute forces (spring, PEF and K-fibre) averaged over trajectories (and sisters). (B) Mean force partition averaged over all time-points, time-points where the kinetochore is attached to a polymerising or depolymerising K-fibre. Absolute force values are averaged over respective sister and time-points across all trajectories (<i>n</i> = 843).</p

Force profiles during directional switches.

<p>Time-dependent estimation of (A) spring force and (B) PEF. Switch events from trajectories where the lead (LIDS, <i>n</i> = 1614; magenta) or trail (TIDS, <i>n</i> = 449; black) kinetochore switched first were aligned at their median switching time (the time origin is marked by vertical blue line). Solid lines indicate mean, dashed lines ±s.e.m.. Forces are given as velocities by rescaling by the viscosity coefficient. (C) Opposing force (spring + PEF) on lead sister during a LIDS (magenta) or TIDS (black) is plotted relative to the depolymerisation force <i>F</i>₋. Solid lines indicate mean, dashed lines 5% and 95% percentiles of the population. (D) Spring force heat map across switching events partitioned by difference between probability of LIDS (<i>p</i>_LIDS) or TIDS (<i>p</i>_TIDS). Events above zero on right <i>y</i>-axis are classified as LIDS; those below as TIDS.</p