Divergent Aging of Isogenic Yeast Cells Revealed through Single-Cell Phenotypic Dynamics

Jin, Meng; Li, Yang; O’Laughlin, Richard; Bittihn, Philip; Pillus, Lorraine; Tsimring, Lev S.; Hasty, Jeff; Hao, Nan

doi:10.1016/j.cels.2019.02.002

Cited by 45 publications

(53 citation statements)

References 37 publications

(43 reference statements)

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“…A recent work proposed a morphological landscape of yeast aging on which the mother cells took two distinguishable aging paths: in the first path the mother cells produced enlarged and elongated daughter cells, and in the second path the mother cells produced small and round daughter cells (Jin et al 2019). The mother cells went through the first path had longer RLSs than the mother cells underwent the second path.…”

Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

“…The mother cells went through the first path had longer RLSs than the mother cells underwent the second path. Two morphologies were used in this landscape to infer the aging paths including the daughterto-mother size ratios for daughter cell size and the axis ratios of the daughter cells for daughter cell roundness, both at the late stages of cell growths (Jin et al 2019;Li et al 2020). Here, we combined the RLSs and took advantage of the Saccharomyces Cerevisiae Morphological Database (SCMD2) which recorded 501 morphologies of different yeast single-gene deletion mutants (Saito et al 2004;Ohya et al 2005).…”

Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

“…Here, we combined the RLSs and took advantage of the Saccharomyces Cerevisiae Morphological Database (SCMD2) which recorded 501 morphologies of different yeast single-gene deletion mutants (Saito et al 2004;Ohya et al 2005). Figure 4 showed both the protein abundance-based and gene expression-based PIPLs of RLS versus morphologies obtained from the SCMD2 including the daughter-to-mother size ratios (C118_C) and daughter axis ratios (C114_C) at the late stages of cell growth from the SCMD, both had been used in the construction of the morphological landscape (Jin et al 2019). Obvious differences between the landscapes based on protein abundances and those based on gene expressions could be found, similar to the landscapes shown above.…”

Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

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Protein interaction probability landscapes for yeast replicative aging

Guo

Dang

Qin

2020

Preprint

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We used probability landscapes to map the age-dependent protein-protein interaction profiles during Saccharomyces cerevisiae replicative aging. We used quasi-potentials, the negative logarithm of the probabilities, to visualize the downs and rises on the landscapes with respect to the young cells. When the interaction probability is gauged by protein abundance during aging, intra-essential protein interactions fell into a low-potential basin. When the interaction probability is estimated from gene expression, we observed that intra-essential protein interactions were located on a high-potential ridge. These opposite patterns coincide with the previously reported uncoupling of the transcriptome and proteome during yeast aging and were also observed in landscapes on different parameter spaces. A proteome-wide probability landscape revealed that the essential proteins favor the interactions with other essential proteins. The increases in essential hub protein abundances may partly explain the opposite trends observed in the landscapes.

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Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

Section: Pipl On Dimensions Of Rls Versus Morphologymentioning

confidence: 99%

See 1 more Smart Citation

Protein interaction probability landscapes for yeast replicative aging

Guo

Dang

Qin

2020

Preprint

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“…The methodological advances achieved during the last decade allowed the study of phenotype heterogeneity in clonal cells. Particularly, the use of microorganism models has challenged the classical view of the phenotype determinacy based on the genotype and environmental context [1][2][3][4][5] .…”

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confidence: 99%

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

Lopes

Cruz²,

Rodrigues

et al. 2020

Sci Rep

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phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. to assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. these can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence. open Scientific RepoRtS | (2020) 10:2362 | https://doi.org/10.1038/s41598-020-59276-w www.nature.com/scientificreports www.nature.com/scientificreports/ yeast kill 180,000 people around the world every year 13 . In addition, the treatment of cryptococcosis is unaffordable in most in developing countries 14,15 .An important trait for cryptococcal virulence is the presence of a polysaccharide capsule, which has been well described at the molecular and functional levels 16,17 . Capsular morphology includes a huge heterogeneity among distinct isolates and even in clonal isolates. This cellular diversity has been exploited in terms of drug resistance and pathogenic potential [18][19][20] .The study of phenotype heterogeneity is hampered by the lack of proper detection methods and statistical analyses. In order to further explore this morphotype diversity, we implemented an automated image analysis pipeline. This machine learning approach is capable of detecting and classifying capsular morphotypes, being applicable to cell type quantification in microscopy-based experiments. While not a high-throughput technique per se, SEM does yield vast amounts of complex data. Here, we describe the adaptation of one algorithmic implementation for the analysis and classification of Cryptococcus spp. capsular morphotypes captured using scanning electron microscopy (SEM). Our model substantially increases data analysis efficiency and provides a template for future machine learning applications within microbiology. ResultsCryptococcus spp. exhibit distinct capsule morphotypes under scanning electron microscopy. The analysis of Cryptococcus s...

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“…Using single-cell imaging technologies, we found that isogenic WT cells exhibit two different types of phenotypic changes during aging (6,7). About half of aging cells continuously produced daughters with a characteristic elongated morphology during later stages of lifespan.…”

mentioning

confidence: 99%

An epigenetic landscape governs early fate decision in cellular aging

Jiang

Paxman

et al. 2019

Preprint

Self Cite

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Chromatin instability and mitochondrial decline are conserved processes that contribute to cellular aging. Although both processes have been explored individually in the context of their distinct signaling pathways, the mechanism that determines which cell fate arises in isogenic cells is unknown. Here, we show that interactions between the chromatin silencing and mitochondrial pathways lead to an epigenetic landscape with multiple equilibrium states that represent different types of terminal cellular states. Interestingly, the structure of the landscape drives single-cell differentiation towards one of these states during aging, whereby the fate is determined quite early and is insensitive to intracellular noise. Guided by a quantitative model of the aging landscape, we genetically engineer a new "long-lived" equilibrium state that is characterized by a dramatically extended lifespan.

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Divergent Aging of Isogenic Yeast Cells Revealed through Single-Cell Phenotypic Dynamics

Cited by 45 publications

References 37 publications

Protein interaction probability landscapes for yeast replicative aging

Protein interaction probability landscapes for yeast replicative aging

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

An epigenetic landscape governs early fate decision in cellular aging

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