scholarly journals Image-derived models of cell organization changes during differentiation and drug treatments

2020 ◽  
Vol 31 (7) ◽  
pp. 655-666 ◽  
Author(s):  
Xiongtao Ruan ◽  
Gregory R. Johnson ◽  
Iris Bierschenk ◽  
Roland Nitschke ◽  
Melanie Boerries ◽  
...  
Keyword(s):  
Single Cell ◽  
Pc12 Cells ◽  
Cell Shape ◽  
Nuclear Shape ◽  
Extensive Cell ◽  
Cell Organization ◽  
Drug Treatments ◽  
Mitochondrial Distribution ◽  
Cell Variation ◽  
Time Point

Images of differentiating PC12 cells were used to construct models of cell shape, nuclear shape, and mitochondrial distribution. Likely trajectories that a single cell would have followed if it had been observed at each time point were found, and synthetic movies made that show the extensive cell–cell variation in rate and extent of differentiation.

scholarly journals Image-derived Models of Cell Organization Changes During Differentiation of PC12 Cells

2019 ◽  
Author(s):  
Xiongtao Ruan ◽  
Gregory R. Johnson ◽  
Iris Bierschenk ◽  
Roland Nitschke ◽  
Melanie Boerries ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Pc12 Cells ◽  
Cellular Differentiation ◽  
Predictive Accuracy ◽  
Single Cells ◽  
Fluorescent Microscopy ◽  
Generative Model ◽  
Nuclear Shape ◽  
Cell Organization ◽  
Mitochondrial Distribution

AbstractCellular differentiation is a complex process requiring the coordination of many cellular components. PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While significant attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in cell organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about this, although photobleaching and phototoxicity frequently limit the ability to continuously observe changes in single cells over the many days that differentiation occurs. Here we describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that our spherical harmonic-based model can accurately represent cell and nuclear shapes by measuring reconstruction errors. We then learn a regression model that relates cell and nuclear shape and mitochondrial distribution and observe that the predictive accuracy generally increases during differentiation. Most importantly, we propose a method, based on cell matching and linear interpolation in the shape space, to model the dynamics of cell differentiation using only static images. Without any prior knowledge, the method produces a realistic shape evolution process.Author SummaryCellular differentiation is an important process that is challenging to study due to the number of organizational changes it includes and the different time scales over which it occurs. Fluorescent microscopy is widely used to study cell dynamics and differentiation, but photobleaching and phototoxicity often make it infeasible to continuously observe a single cell undergoing differentiation for several days. In this work, we described a method to model aspects of the dynamics of PC12 cell differentiation without continuous imaging. We constructed accurate representations of cell and nuclear shapes and quantified the relationships between shapes and mitochondrial distributions. We used these to construct a generative model and combined it with a matching process to infer likely sequences of the changes in single cells undergoing differentiation.

scholarly journals The Ethanologenic Bacterium Zymomonas mobilis Divides Asymmetrically and Exhibits Heterogeneity in DNA Content

2021 ◽  
Vol 87 (6) ◽  
Author(s):  
Katsuya Fuchino ◽  
Helena Chan ◽  
Ling Chin Hwang ◽  
Per Bruheim
Keyword(s):  
Cell Growth ◽  
Single Cell ◽  
Cell Size ◽  
Dna Content ◽  
Bacterial Cell ◽  
Zymomonas Mobilis ◽  
Biofuel Production ◽  
Public Attention ◽  
Content Type ◽  
Cell Organization

ABSTRACT The alphaproteobacterium Zymomonas mobilis exhibits extreme ethanologenic physiology, making this species a promising biofuel producer. Numerous studies have investigated its biology relevant to industrial applications and mostly at the population level. However, the organization of single cells in this industrially important polyploid species has been largely uncharacterized. In the present study, we characterized basic cellular behavior of Z. mobilis strain Zm6 under anaerobic conditions at the single-cell level. We observed that growing Z. mobilis cells often divided at a nonmidcell position, which contributed to variant cell size at birth. However, the cell size variance was regulated by a modulation of cell cycle span, mediated by a correlation of bacterial tubulin homologue FtsZ ring accumulation with cell growth. The Z. mobilis culture also exhibited heterogeneous cellular DNA content among individual cells, which might have been caused by asynchronous replication of chromosome that was not coordinated with cell growth. Furthermore, slightly angled divisions might have resulted in temporary curvatures of attached Z. mobilis cells. Overall, the present study uncovers a novel bacterial cell organization in Z. mobilis. IMPORTANCE With increasing environmental concerns about the use of fossil fuels, development of a sustainable biofuel production platform has been attracting significant public attention. Ethanologenic Z. mobilis species are endowed with an efficient ethanol fermentation capacity that surpasses, in several respects, that of baker’s yeast (Saccharomyces cerevisiae), the most-used microorganism for ethanol production. For development of a Z. mobilis culture-based biorefinery, an investigation of its uncharacterized cell biology is important, because bacterial cellular organization and metabolism are closely associated with each other in a single cell compartment. In addition, the current work demonstrates that the polyploid bacterium Z. mobilis exhibits a distinctive mode of bacterial cell organization, likely reflecting its unique metabolism that does not prioritize incorporation of nutrients for cell growth. Thus, another significant result of this work is to advance our general understanding in the diversity of bacterial cell architecture.

scholarly journals A deep generative model of 3D single-cell organization

2021 ◽  
Author(s):  
Rory Donovan-Maiye ◽  
Jackson Brown ◽  
Caleb Chan ◽  
Liya Ding ◽  
Calysta Yan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Morphology ◽  
Image Data ◽  
Subcellular Structure ◽  
Cell Image ◽  
Fluorescent Image ◽  
Trade Offs ◽  
Cell Organization ◽  
Latent Representations ◽  
Target Effects

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

scholarly journals Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear. Results: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Cell Shape and Population Migration Are Distinct Steps ofProteus mirabilisSwarming That Are Decoupled on High-Percentage Agar

2019 ◽  
Vol 201 (11) ◽  
Author(s):  
Kristin Little ◽  
Jacob Austerman ◽  
Jenny Zheng ◽  
Karine A. Gibbs
Keyword(s):  
Cell Motility ◽  
Single Cell ◽  
Cell Elongation ◽  
Cell Shape ◽  
Population Migration ◽  
Content Type ◽  
Agar Concentration ◽  
Collective Interactions ◽  
Cell Phenotypes ◽  
Rigid Surfaces

ABSTRACTSwarming on rigid surfaces requires movement of cells as individuals and as a group of cells. For the bacteriumProteus mirabilis, an individual cell can respond to a rigid surface by elongating and migrating over micrometer-scale distances. Cells can form groups of transiently aligned cells, and the collective population is capable of migrating over centimeter-scale distances. To address howP. mirabilispopulations swarm on rigid surfaces, we asked whether cell elongation and single-cell motility are coupled to population migration. We first measured the relationship between agar concentration (a proxy for surface rigidity), single-cell phenotypes, and swarm colony phenotypes. We find that cell elongation and single-cell motility are coupled with population migration on low-percentage hard agar (1% to 2.5%) and become decoupled on high-percentage hard agar (>2.5%). Next, we evaluate how disruptions in lipopolysaccharide (LPS), specifically the O-antigen components, affect responses to hard agar. We find that LPS is not essential for elongation and motility of individual cells, as predicted, and instead functions to broaden the range of agar concentrations on which cell elongation and motility are coupled with population migration. These findings demonstrate that cell elongation and motility are coupled with population migration under a permissive range of surface conditions; increasing agar concentration is sufficient to decouple these behaviors. Since swarm colonies cover greater distances when these steps are coupled than when they are not, these findings suggest that collective interactions amongP. mirabiliscells might be emerging as a colony expands outwards on rigid surfaces.IMPORTANCEHow surfaces influence cell size, cell-cell interactions, and population migration for robust swarmers likeP. mirabilisis not fully understood. Here, we have elucidated how cells change length along a spectrum of sizes that positively correlates with increases in agar concentration, regardless of population migration. Single-cell phenotypes can be decoupled from collective population migration simply by increasing agar concentration. A cell’s lipopolysaccharides function to broaden the range of agar conditions under which cell elongation and single-cell motility remain coupled with population migration. In eukaryotes, the physical environment, such as a surface matrix, can impact cell development, shape, and migration. These findings support the idea that rigid surfaces similarly act on swarming bacteria to impact cell shape, single-cell motility, and collective population migration.

scholarly journals Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

2018 ◽  
Vol 20 (4) ◽  
pp. 1583-1589 ◽  
Author(s):  
Shun H Yip ◽  
Pak Chung Sham ◽  
Junwen Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differentially Expressed Gene ◽  
Embryonic Stem ◽  
Rna Seq ◽  
Software Packages ◽  
Homogeneous Cell ◽  
Variable Gene ◽  
Cell Variation ◽  
Larger Sample

Abstract Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

scholarly journals Delineating the heterogeneity of matrix-directed differentiation toward soft and stiff tissue lineages via single-cell profiling

2021 ◽  
Vol 118 (19) ◽  
pp. e2016322118
Author(s):  
Shlomi Brielle ◽  
Danny Bavli ◽  
Alex Motzik ◽  
Yoav Kan-Tor ◽  
Xue Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Nuclear Localization ◽  
Endochondral Ossification ◽  
Target Genes ◽  
Induction Medium ◽  
Actin Stress Fiber ◽  
Directed Differentiation ◽  
Applied Forces ◽  
Cell Variation ◽  
Msc Differentiation

Mesenchymal stromal/stem cells (MSCs) form a heterogeneous population of multipotent progenitors that contribute to tissue regeneration and homeostasis. MSCs assess extracellular elasticity by probing resistance to applied forces via adhesion, cytoskeletal, and nuclear mechanotransducers that direct differentiation toward soft or stiff tissue lineages. Even under controlled culture conditions, MSC differentiation exhibits substantial cell-to-cell variation that remains poorly characterized. By single-cell transcriptional profiling of nonconditioned, matrix-conditioned, and early differentiating cells, we identified distinct MSC subpopulations with distinct mechanosensitivities, differentiation capacities, and cell cycling. We show that soft matrices support adipogenesis of multipotent cells and early endochondral ossification of nonadipogenic cells, whereas intramembranous ossification and preosteoblast proliferation are directed by stiff matrices. Using diffusion pseudotime mapping, we outline hierarchical matrix-directed differentiation and perform whole-genome screening of mechanoresponsive genes. Specifically, top-ranked tropomyosin-1 is highly sensitive to stiffness cues both at RNA and protein levels, and changes in TPM1 expression determine the differentiation toward soft versus stiff tissue lineage. Consistent with actin stress fiber stabilization, tropomyosin-1 overexpression maintains YAP1 nuclear localization, activates YAP1 target genes, and directs osteogenic differentiation. Knockdown of tropomyosin-1 reversed YAP1 nuclear localization consistent with relaxation of cellular contractility, suppressed osteogenesis, activated early endochondral ossification genes after 3 d of culture in induction medium, and facilitated adipogenic differentiation after 1 wk. Our results delineate cell-to-cell variation of matrix-directed MSC differentiation and highlight tropomyosin-mediated matrix sensing.

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