scholarly journals Automated deep lineage tree analysis using a Bayesian single cell tracking approach

2020 ◽  
Author(s):  
Kristina Ulicna ◽  
Giulia Vallardi ◽  
Guillaume Charras ◽  
Alan R. Lowe
Keyword(s):  
Cell Cycle ◽  
Stochastic Processes ◽  
Single Cell ◽  
Family Relationships ◽  
Cell Tracking ◽  
Molecular Mechanisms ◽  
Live Cell ◽  
Cell Populations ◽  
Tree Reconstruction ◽  
Lineage Tree

ABSTRACTSingle-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, which arises from the interplay or deterministic and stochastic processes. For example, the molecular mechanisms of cell cycle control are well characterised, yet the observed distribution of cell cycle durations in a population of cells is heterogenous. This variability may be governed either by stochastic processes, inherited in a deterministic fashion, or some combination of both. Previous studies have shown poor correlations within lineages when observing direct ancestral relationships but remain correlated with immediate relatives. However, assessing longer-range dependencies amid noisy data requires significantly more observations, and demands the development of automated procedures for lineage tree reconstruction. Here, we developed an open-source Python library, btrack, to facilitate retrieval of deep lineage information from live-cell imaging data. We acquired 3,500 hours of time-lapse microscopy data of epithelial cells in culture and used our software to extract 22,519 fully annotated single-cell trajectories. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results and achieves state-of-the-art performance without the requirement for manual curation of the tracker output data. To demonstrate the robustness of our supervision-free cell tracking pipeline, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships, for up to eight generations, and up to fourth cousin relationships. The extracted lineage tree dataset represents approximately two orders of magnitude more data, and longer-range dependencies, than in previous studies of cell cycle heritability. Our results extend the range of observed correlations and suggest that strong heritable cell cycling is present. We envisage that our approach could be extended with additional live-cell reporters to provide a detailed quantitative characterisation of biochemical and mechanical origins to cycling heterogeneity in cell populations.

scholarly journals Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Cell Tracking ◽  
Time Lapse ◽  
Live Cell ◽  
Time Lapse Imaging

scholarly journals Modeling progression of single cell populations through the cell cycle as a sequence of switches

2021 ◽  
Author(s):  
Andrei Zinovyev ◽  
Michail Sadovsky ◽  
Laurence Calzone ◽  
Aziz Fouché ◽  
Clarice S Groeneveld ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
System Size ◽  
Mechanistic Modeling ◽  
Cell Populations ◽  
Dynamical Process ◽  
Modeling Framework ◽  
Internal States ◽  
Fundamental Biological Process

Cell cycle is the most fundamental biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We show how the developed formalism can be applied to model the available single cell datasets and simulate certain properties of the cell cycle trajectories.

scholarly journals Single-cell RNA sequencing reveals differential cell cycle activity in key cell populations during nephrogenesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abha S. Bais ◽  
Débora M. Cerqueira ◽  
Andrew Clugston ◽  
Andrew J. Bodnar ◽  
Jacqueline Ho ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Kidney Development ◽  
Collecting Duct ◽  
Cell Types ◽  
Nephron Progenitors ◽  
Developing Kidney ◽  
Distal Tubules ◽  
Cycle Regulation

AbstractThe kidney is a complex organ composed of more than 30 terminally differentiated cell types that all are required to perform its numerous homeostatic functions. Defects in kidney development are a significant cause of chronic kidney disease in children, which can lead to kidney failure that can only be treated by transplant or dialysis. A better understanding of molecular mechanisms that drive kidney development is important for designing strategies to enhance renal repair and regeneration. In this study, we profiled gene expression in the developing mouse kidney at embryonic day 14.5 at single-cell resolution. Consistent with previous studies, clusters with distinct transcriptional signatures clearly identify major compartments and cell types of the developing kidney. Cell cycle activity distinguishes between the “primed” and “self-renewing” sub-populations of nephron progenitors, with increased expression of the cell cycle-related genes Birc5, Cdca3, Smc2 and Smc4 in “primed” nephron progenitors. In addition, augmented expression of cell cycle related genes Birc5, Cks2, Ccnb1, Ccnd1 and Tuba1a/b was detected in immature distal tubules, suggesting cell cycle regulation may be required for early events of nephron patterning and tubular fusion between the distal nephron and collecting duct epithelia.

scholarly journals Classic “broken cell” techniques and newer live cell methods for cell cycle assessment

2013 ◽  
Vol 304 (10) ◽  
pp. C927-C938 ◽  
Author(s):  
Lindsay Henderson ◽  
Dante S. Bortone ◽  
Curtis Lim ◽  
Alexander C. Zambon
Keyword(s):  
Cell Cycle ◽  
Cell Division ◽  
Single Cell ◽  
Living Cells ◽  
Cell Division Cycle ◽  
Live Cell ◽  
Nucleotide Analogs ◽  
Protein Ubiquitination ◽  
Cell Cycle Pathway ◽  
Cell Niche

Many common, important diseases are either caused or exacerbated by hyperactivation (e.g., cancer) or inactivation (e.g., heart failure) of the cell division cycle. A better understanding of the cell cycle is critical for interpreting numerous types of physiological changes in cells. Moreover, new insights into how to control it will facilitate new therapeutics for a variety of diseases and new avenues in regenerative medicine. The progression of cells through the four main phases of their division cycle [G0/G1, S (DNA synthesis), G2, and M (mitosis)] is a highly conserved process orchestrated by several pathways (e.g., transcription, phosphorylation, nuclear import/export, and protein ubiquitination) that coordinate a core cell cycle pathway. This core pathway can also receive inputs that are cell type and cell niche dependent. “Broken cell” methods (e.g., use of labeled nucleotide analogs) to assess for cell cycle activity have revealed important insights regarding the cell cycle but lack the ability to assess living cells in real time (longitudinal studies) and with single-cell resolution. Moreover, such methods often require cell synchronization, which can perturb the pathway under study. Live cell cycle sensors can be used at single-cell resolution in living cells, intact tissue, and whole animals. Use of these more recently available sensors has the potential to reveal physiologically relevant insights regarding the normal and perturbed cell division cycle.

scholarly journals Automated cell cycle and cell size measurements for single-cell gene expression studies

2017 ◽  
Author(s):  
Anissa Guillemin ◽  
Angelique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Size ◽  
Cell Tracking ◽  
Erythroid Progenitors ◽  
Expression Variability ◽  
Unbiased Gene ◽  
Transcriptomic Level ◽  
Cell To Cell Variability

AbstractRecent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

scholarly journals Single-Cell RNA Sequencing Defines the Regulation of Spermatogenesis by Sertoli-Cell Androgen Signaling

2021 ◽  
Vol 9 ◽  
Author(s):  
Congcong Cao ◽  
Qian Ma ◽  
Shaomei Mo ◽  
Ge Shu ◽  
Qunlong Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Sertoli Cells ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Wild Type ◽  
Cellular Processes ◽  
Transcriptomic Sequencing ◽  
Transcriptional Changes

Androgen receptor (AR) signaling is essential for maintaining spermatogenesis and male fertility. However, the molecular mechanisms by which AR acts between male germ cells and somatic cells during spermatogenesis have not begun to be revealed until recently. With the advances obtained from the use of transgenic mice lacking AR in Sertoli cells (SCARKO) and single-cell transcriptomic sequencing (scRNA-seq), the cell specific targets of AR action as well as the genes and signaling pathways that are regulated by AR are being identified. In this study, we collected scRNA-seq data from wild-type (WT) and SCARKO mice testes at p20 and identified four somatic cell populations and two male germ cell populations. Further analysis identified that the distribution of Sertoli cells was completely different and uncovered the cellular heterogeneity and transcriptional changes between WT and SCARKO Sertoli cells. In addition, several differentially expressed genes (DEGs) in SCARKO Sertoli cells, many of which have been previously implicated in cell cycle, apoptosis and male infertility, have also been identified. Together, our research explores a novel perspective on the changes in the transcription level of various cell types between WT and SCARKO mice testes, providing new insights for the investigations of the molecular and cellular processes regulated by AR signaling in Sertoli cells.

scholarly journals Dissecting the initiation of female meiosis in the mouse at single-cell resolution

2019 ◽  
Author(s):  
Wei Ge ◽  
Jun-Jie Wang ◽  
Rui-Qian Zhang ◽  
Shao-Jing Tan ◽  
Fa-Li Zhang ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Germ Cells ◽  
Molecular Mechanisms ◽  
Gene Expression Pattern ◽  
Cell Populations ◽  
Molecular Pathways ◽  
Cell Clusters ◽  
Prophase I ◽  
New Genes

ABSTRACTGerm cell meiosis is one of the most finely orchestrated events during gametogenesis with distinct developmental patterns in males and females. However, in mammals, the molecular mechanisms involved in this process remain not well known. Here, we report detailed transcriptome analyses of cell populations present in the mouse female gonadal ridges (E11.5) and the embryonic ovaries from E12.5 to E14.5 using single cell RNA sequencing (scRNA seq). These periods correspond with the initiation and progression of meiosis throughout the first stage of prophase I. We identified 13 transcriptionally distinct cell populations and 7 transcriptionally distinct germ cell subclusters that correspond to mitotic (3 clusters) and meiotic (4 clusters) germ cells. By comparing the signature gene expression pattern of 4 meiotic germ cell clusters, we found that the 4 cell clusters correspond to different cell status en route to meiosis progression, and therefore, our research here characterized detailed transcriptome dynamics during meiotic prophase I. Reconstructing the progression of meiosis along pseudotime, we identified several new genes and molecular pathways with potential critical roles in the mitosis/meiosis transition and early meiotic progression. Last, the heterogeneity within somatic cell populations was also discussed and different cellular states were identified. Our scRNA seq analysis here represents a new important resource for deciphering the molecular pathways driving meiosis initiation and progression in female germ cells and ovarian somatic cells.

scholarly journals Scelestial: fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm

2021 ◽  
Author(s):  
Mohammad-Hadi Foroughmand-Araabi ◽  
Sama Goliaei ◽  
Alice Carolyn McHardy
Keyword(s):  
Approximation Algorithm ◽  
Single Cell ◽  
Steiner Tree ◽  
Missing Values ◽  
Cell Lineage ◽  
Error Rates ◽  
Steiner Tree Problem ◽  
Tree Reconstruction ◽  
Tree Inference ◽  
Lineage Tree

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms - BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit - on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.

scholarly journals Computationally Enhanced Quantitative Phase Microscopy Reveals Autonomous Oscillations in Mammalian Cell Growth

2019 ◽  
Author(s):  
Xili Liu ◽  
Seungeun Oh ◽  
Leonid Peshkin ◽  
Marc W. Kirschner
Keyword(s):  
Cell Cycle ◽  
Growth Rate ◽  
Cell Growth ◽  
Single Cell ◽  
Cell Lines ◽  
Mammalian Cells ◽  
Cell Tracking ◽  
Quantitative Phase ◽  
Phase Microscopy ◽  
Quantitative Phase Microscopy

AbstractThe fine balance of growth and division is a fundamental property of the physiology of cells and one of the least understood. Its study has been thwarted by difficulties in the accurate measurement of cell size and the even greater challenges of measuring growth of a single-cell over time. We address these limitations by demonstrating a new computationally enhanced methodology for Quantitative Phase Microscopy (ceQPM) for adherent cells, using improved image processing algorithms and automated cell tracking software. Accuracy has been improved more than two-fold and this improvement is sufficient to establish the dynamics of cell growth and adherence to simple growth laws. It is also sufficient to reveal unknown features of cell growth previously unmeasurable. With these methodological and analytical improvements, we document a remarkable oscillation in growth rate in several different cell lines, occurring throughout the cell cycle, coupled to cell division or birth, and yet independent of cell cycle progression. We expect that further exploration with this improved tool will provide a better understanding of growth rate regulation in mammalian cells.Significance StatementIt has been a long-standing question in cell growth studies that whether the mass of individual cell grows linearly or exponentially. The two models imply fundamentally distinct mechanisms, and the discrimination of the two requires great measurement accuracy. Here, we develop a new method of computationally enhanced Quantitative Phase Microscopy (ceQPM), which greatly improves the accuracy and throughput of single-cell growth measurement in adherent mammalian cells. The measurements of several cell lines indicate that the growth dynamics of individual cells cannot be explained by either of the simple models but rather present an unanticipated and remarkable oscillatory behavior, suggesting more complex regulation and feedbacks.

scholarly journals Identification of SNA-I-positive cells as stem-like cells in an established cell line using computerized single-cell lineage tracking

2018 ◽  
Author(s):  
Sachiko Sato ◽  
Ann Rancourt ◽  
Masahiko S. Satoh
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Population ◽  
Cell Tracking ◽  
Cell Lineage ◽  
Live Cell ◽  
Wide Range ◽  
A Cell ◽  
Analysis System ◽  
Lineage Tracking

AbstractSingle-cell tracking analysis is a potential research technique for the accurate investigation of cellular behaviors and events occurring within a cell population. However, this analysis is challenging because of a lack of microscope hardware and software suitable for single-cell tracking analysis of a wide range of cell types and densities. We therefore developed a computerized single-cell lineage tracking analysis system based on a microscope optimized for differential interference contrast-based long-term live cell imaging, with software designed to automatically generate live cell videos, perform image segmentation, carry out single-cell tracking, and create and analyze a cell lineage database. We previously reported that minor cell sub-populations (3%–7%) within a cultured cancer cell line could play a critical role in maintaining the cell population. Given that sub-population characterization requires large-scale single-cell tracking analysis, we tracked single cells using the above computerized system and identified a minor cell population (1.5%) composed ofSambucus nigraagglutinin-I-positive cells, which acted as stem-like cells for the established culture. These results demonstrate the potential value of this computerized single-cell lineage tracking analysis system as a routine tool in cell biology, opening new avenues for research aimed at identifying previously unknown characteristics of individual cultured cells with high accuracy.

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