Universal constraints on selection strength in lineage trees

Abstract Human cancers display substantial intratumoral genetic heterogeneity, which facilitates tumor survival under changing microenvironmental conditions. Tumor substructure and its effect on disease progression and relapse are incompletely understood. In the present study, a high-throughput method that uses neutral somatic mutations accumulated in individual cells to reconstruct cell lineage trees was applied to hundreds of cells of human acute leukemia harvested from multiple patients at diagnosis and at relapse. The reconstructed cell lineage trees of patients with acute myeloid leukemia showed that leukemia cells at relapse were shallow (divide rarely) compared with cells at diagnosis and were closely related to their stem cell subpopulation, implying that in these instances relapse might have originated from rarely dividing stem cells. In contrast, among patients with acute lymphoid leukemia, no differences in cell depth were observed between diagnosis and relapse. In one case of chronic myeloid leukemia, at blast crisis, most of the cells at relapse were mismatch-repair deficient. In almost all leukemia cases, > 1 lineage was observed at relapse, indicating that diverse mechanisms can promote relapse in the same patient. In conclusion, diverse relapse mechanisms can be observed by systematic reconstruction of cell lineage trees of patients with leukemia.

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Cell Tracking for Organoids: Lessons From Developmental Biology

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.675013 ◽

2021 ◽

Vol 9 ◽

Author(s):

Max A. Betjes ◽

Xuan Zheng ◽

Rutger N. U. Kok ◽

Jeroen S. van Zon ◽

Sander J. Tans

Keyword(s):

Cell Tracking ◽

Cellular Level ◽

Model Systems ◽

Great Promise ◽

Cellular Processes ◽

The Past ◽

Fluorescent Markers ◽

Lineage Trees ◽

Microscopy Techniques ◽

Organizational Principles

Organoids have emerged as powerful model systems to study organ development and regeneration at the cellular level. Recently developed microscopy techniques that track individual cells through space and time hold great promise to elucidate the organizational principles of organs and organoids. Applied extensively in the past decade to embryo development and 2D cell cultures, cell tracking can reveal the cellular lineage trees, proliferation rates, and their spatial distributions, while fluorescent markers indicate differentiation events and other cellular processes. Here, we review a number of recent studies that exemplify the power of this approach, and illustrate its potential to organoid research. We will discuss promising future routes, and the key technical challenges that need to be overcome to apply cell tracking techniques to organoid biology.

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Early developmental asymmetries in cell lineage trees in living individuals

10.1101/2020.08.24.265751 ◽

2020 ◽

Author(s):

Liana Fasching ◽

Yeongjun Jang ◽

Simone Tomasi ◽

Jeremy Schreiner ◽

Livia Tomasini ◽

...

Keyword(s):

Cell Fate ◽

Cell Lineage ◽

Postmortem Brain ◽

Cell Lineages ◽

Human Specimen ◽

Balanced Contributions ◽

Health And Disease ◽

Induced Pluripotent ◽

Lineage Trees ◽

Early Developmental

AbstractPost-zygotic mosaic mutations can be used to track cell lineages in humans. By using cell cloning and induced pluripotent cell lines, we analyzed early cell lineages in two living individuals (a patient and a control), and a postmortem human specimen. Of ten reconstructed post-zygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. Recurrence of germline variants as mosaic suggested that certain loci may be particularly susceptible to mutagenesis. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.

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Design Analysis of Chassis used in Self-Propelled Onion Harvester using FEA Tool

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35650 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3099-3105

Author(s):

Kiran Gosavi

Keyword(s):

Fuel Economy ◽

Factor Of Safety ◽

Material Selection ◽

High Yield ◽

Force Distribution ◽

Element Analysis ◽

Pros And Cons ◽

Induced Stresses ◽

Selection Strength ◽

Comparative Results

Onion farming is more commonly practiced for an irrigated crop, resulting in a high yield with large sized bulbs. Manual harvesting of an onion being meticulous requires a large amount of manpower as well as time. Thus, we have constructed and evaluated a self-propelled onion harvester which will have good performance in terms of productivity, fuel economy, less damage to crop and operator comfort. This paper is intended to discuss the results of the design and analysis of the chassis under the guidelines of the SAE TIFAN rulebook [1]. The chassis is designed using tool CATIA V5 followed by Finite element analysis (FEA) using ANSYS and the consequent results have been plotted and comparative results of old and modified chassis has proposed. During chassis designing and analysis, several factors are taken into account like material selection, strength, durability, boundary conditions, force distribution, induced stresses, optimum factor of safety, ergonomics and aesthetics. All the decisions for design are based on all pros and cons from testing and results of previous competitions.

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Lineage EM Algorithm for Inferring Latent States from Cellular Lineage Trees

10.1101/488981 ◽

2018 ◽

Author(s):

So Nakashima ◽

Yuki Sughiyama ◽

Tetsuya J. Kobayashi

Keyword(s):

Em Algorithm ◽

Latent Variable ◽

Phenotypic Variability ◽

Estimation Algorithm ◽

Bet Hedging ◽

Alternative Approach ◽

Survivorship Bias ◽

Survivor Bias ◽

Lineage Trees ◽

Statistical Estimations

Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivor bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call Lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.

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Long-range memory of growth and cycle progression correlates cell cycles in lineage trees

10.1101/373258 ◽

2018 ◽

Cited By ~ 5

Author(s):

Erika E Kuchen ◽

Nils Becker ◽

Nina Claudino ◽

Thomas Höfer

Keyword(s):

Cell Cycle ◽

Cell Proliferation ◽

Cell Growth ◽

Mammalian Cell ◽

Latent Variable ◽

Cycle Length ◽

Growth Control ◽

Minimum Size ◽

Cell Cycles ◽

Lineage Trees

Mammalian cell proliferation is controlled by mitogens. However, how proliferation is coordinated with cell growth is poorly understood. Here we show that statistical properties of cell lineage trees – the cell-cycle length correlations within and across generations – reveal how cell growth controls proliferation. Analyzing extended lineage trees with latent-variable models, we find that two antagonistic heritable variables account for the observed cycle-length correlations. Using molecular perturbations of mTOR and MYC we identify these variables as cell size and regulatory license to divide, which are coupled through a minimum-size checkpoint. The checkpoint is relevant only for fast cell cycles, explaining why growth control of mammalian cell proliferation has remained elusive. Thus, correlated fluctuations of the cell cycle encode its regulation.

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Adaptation and Natural Selection

Population Genetics with R ◽

10.1093/oso/9780198829539.003.0007 ◽

2021 ◽

pp. 125-154

Author(s):

Áki J. Láruson ◽

Floyd A. Reed

Keyword(s):

Natural Selection ◽

Allele Frequency ◽

Levels Of Selection ◽

Frequency Data ◽

Selection Coefficient ◽

Frequency Distributions ◽

Single Allele ◽

Selection Strength ◽

The Difference ◽

Over Time

Here non-random shifts in allele frequencies over time are introduced, as well as how to incorporate varying levels of selection into a model of a single population through time. This chapter highlights the difference between weak and strong selection, the dynamics of single allele versus genotype-level selection, and how selection strength and population size affect allele frequency distributions over time. Finally the inference of the selection coefficient from allele frequency data is discussed, alongside the concepts of overdominance and underdominance.

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