Asymmetric binomial statistics explains organelle partitioning variance in cancer cell proliferation

Asymmetric inheritance of organelles and compounds between daughter cells is considered a hallmark for differentiation and rejuvenation in stem-like and cancer cells, as much as a mechanism for enhancing resistance in bacteria populations. In non-differentiating homogeneous cancer cells, asymmetric division is still poorly investigated. Here, we present a method based on the binomial partitioning process that allows the measurement of asymmetric organelle partitioning with multiple live cell markers without genetically mutating the cells. We demonstrate our method by measuring simultaneously the partitioning of three cellular elements, i.e., cytoplasm, membrane, and mitochondria in human Jurkat T-cells. We found that although cell cytoplasm is partitioned symmetrically, mitochondria and membrane lipids are asymmetrically partitioned between daughter cells. Moreover, we observe that mitochondria and membrane lipids present a stable positive correlation with cytoplasm, incompatibly with a binomial partition mechanism produced by two independent partitioning processes. Our experimental apparatus, combined with our theoretical framework, could be generalized to different cell kinds, providing a tool for understanding partitioning-driven biological processes.

Asymmetric Binomial Statistics Explains Organelle Partitioning Variance in Cancer Cell Proliferation

ABSTRACTAsymmetric inheritance of organelle and cellular compounds between daughter cells impacts on the phenotypic variability and was found to be a hallmark for differentiation and rejuvenation in stem-like cells as much as a mechanism for enhancing resistance in bacteria populations. Whether the same processes take place in the context of cancer cell lines is still poorly investigated. Here, we present a method that allows the measurement of asymmetric organelle partitioning, and we use it to simultaneously measure the partitioning of three kinds of cellular elements, i.e. cytoplasm, membrane, and mitochondria in a proliferating population of human Jurkat T-cells. For this porpoise, we use multiple live cell markers which permit us both to follow the partitioning process for multiple generations and to investigate the correlations between the partitioning of different cellular constituents. Assuming a minimal model of asymmetric partitioning where cell sub-components are divided according to a biased binomial statistics, we derived exact analytical relationships for the average fluorescence intensity and its fluctuations as a function of the generation, obtaining an excellent agreement with the experimental measurements.We found that although cell cytoplasm is divided symmetrically, mitochondria and membrane lipids are asymmetrically distributed between the two daughter cells and present a stable positive correlation with cytoplasm apportioning, which is incompatible with an independent division mechanism. Therefore, our findings show that asymmetric segregation mechanisms can also arise in cancer cell populations, and that, in this case, membrane lipids and mitochondria do not respectively segregate independently from the cytoplasm. This helps us understand the high phenotypic variability reported in these cancer cell lines. In perspective, this could be particularly relevant in the case of tumor micro-environment diversity, where comprehension of the non-genetic cell heterogeneity could pave the way to novel and more targeted therapies. Moreover, the developed experimental and theoretical apparatus can be easily generalized to different cell kinds and different cell sub-components providing a powerful tool for understanding partitioning-driven heterogeneity.

Quality control failures exceeding the weekly limit (QC FEWL): a simple tool to improve assay error detection

Background Even when a laboratory analyte testing process is in control, routine quality control testing will fail with a frequency that can be predicted by the number of quality control levels used, the run frequency and the control rule employed. We explored whether simply counting the number of assay quality control run failures during a running week, and then objectively determining if there was an excess, could complement daily quality control processes in identifying an out-of-control assay. Methods Binomial statistics were used to determine the threshold number of quality control run failures in any rolling week which would statistically exceed that expected for a particular test. Power function graphs were used to establish error detection (Ped) and false rejection rates compared with popular control rules. Results Identifying quality control failures exceeding the weekly limit (QC FEWL) is a more powerful means of detecting smaller systematic (bias) errors than traditional daily control rules (12s, 13s or 13s/22s/R4s) and markedly superior in detecting smaller random (imprecision) errors while maintaining false identification rates below 2%. Error detection rates also exceeded those using a within- and between-run Westgard multirule (13s/22s/41s/10x). Conclusions Daily review of tests shown to statistically exceed their rolling week limit of expected quality control run failures is more powerful than traditional quality control tools at identifying potential systematic and random test errors and so offers a supplement to daily quality control practices that has no requirement for complex data extraction or manipulation.

Filaments of galaxies as a clue to the origin of ultrahigh-energy cosmic rays

Ultrahigh-energy cosmic rays (UHECRs) are known to come from outside of our Galaxy, but their origin still remains unknown. The Telescope Array (TA) experiment recently identified a hotspot, that is, a high concentration of anisotropic arrival directions of UHECRs with energies above 5.7 Å ~ 1019eV. We report here the presence of filaments of galaxies, connected to the Virgo Cluster, in the sky around the hotspot and a statistically significant correlation between hotspot events and the filaments. With 5-year TA data, the maximum significance of binomial statistics for the correlation is estimated to be 6.1σ at correlation angle 3.4°. The probability that the above significance appears by chance is ~2.0 × 10−8(5.6σ). On the basis of this finding, we suggest a model for the origin of TA hotspot UHECRs; they are produced at sources in the Virgo Cluster, and escape to and propagate along filaments, before they are scattered toward us. This picture requires the filament magnetic fields of strength ≳ 20 nG, which need to be confirmed in future observations.

On the Estimation of Confidence Intervals for Binomial Population Proportions in Astronomy: The Simplicity and Superiority of the Bayesian Approach

I present a critical review of techniques for estimating confidence intervals on binomial population proportions inferred from success counts in small to intermediate samples. Population proportions arise frequently as quantities of interest in astronomical research; for instance, in studies aiming to constrain the bar fraction, active galactic nucleus fraction, supermassive black hole fraction, merger fraction, or red sequence fraction from counts of galaxies exhibiting distinct morphological features or stellar populations. However, two of the most widely-used techniques for estimating binomial confidence intervals — the ‘normal approximation’ and the Clopper & Pearson approach — are liable to misrepresent the degree of statistical uncertainty present under sampling conditions routinely encountered in astronomical surveys, leading to an ineffective use of the experimental data (and, worse, an inefficient use of the resources expended in obtaining that data). Hence, I provide here an overview of the fundamentals of binomial statistics with two principal aims: (I) to reveal the ease with which (Bayesian) binomial confidence intervals with more satisfactory behaviour may be estimated from the quantiles of the beta distribution using modern mathematical software packages (e.g.r, matlab, mathematica, idl, python); and (ii) to demonstrate convincingly the major flaws of both the ‘normal approximation’ and the Clopper & Pearson approach for error estimation.

Ca2+ Dependence of the Binomial Parameters p and n at the Mouse Neuromuscular Junction

The Ca2+ dependence of synaptic quantal release is generally thought to be restricted to probability of vesicular release. However, some studies have suggested that the number of release sites ( n) at the neuromuscular junction (NMJ) is also Ca2+ dependent. In this study, we recorded endplate currents over a wide range of extracellular Ca2+ concentrations and found the expected Ca2+ dependency of release. A graphical technique was used to estimate p (probability of release) and n using standard binomial assumptions. The results suggested n was Ca2+ dependent. The data were simulated using compound binomial statistics with variable n (Ca2+ dependent) or fixed n (Ca2+ independent). With fixed n, successful simulation of increasing Ca2+ required that p increase abruptly at some sites from very low to high values. Successful simulation with variable n required the introduction of previously silent release sites ( p = 0) with high values of p. Thus the success of both simulations required abrupt, large increases of p at a subset of release sites with initially low or zero p. Estimates of the time course of release obtained by deconvolving evoked endplate currents with average miniature endplate currents decreased slightly as Ca2+ increased, thus arguing against sequential release of multiple quanta at higher Ca2+ levels. Our results suggest that the apparent Ca2+ dependence of n at the NMJ can be explained by an underlying Ca2+ dependence of a spatially variable p such that p increases abruptly at a subset of sites as Ca2+ is increased.