Wound-induced Ca<sup>2+</sup>wave propagates through a simple release and diffusion mechanism

Handly, L. Naomi; Wollman, Roy

doi:10.1091/mbc.e16-10-0695

Cited by 37 publications

(35 citation statements)

References 47 publications

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“…Given that the genes we probe are related to Ca 2+ signaling, we first extracted key features from time series of cytoplasmic Ca 2+ response measured with a calibrated GCaMP5 biosensor (Appendix Fig S2A). The live cell imaging of cytoplasmic Ca 2+ levels (Fig C) showed a highly heterogeneous response, qualitatively and quantitatively similar to previous work on Ca 2+ signaling in MCF10A cells where we observed a mixed population response with a wide range of response phenotypes (Yao et al , ; Handly & Wollman, ). We used a feature‐based representation of Ca 2+ response to represent cellular factors that we anticipate correlate with underlying cell state (Fig G and Appendix Fig S2).…”

Section: Resultssupporting

confidence: 88%

“…We used sequential hybridization smFISH (MERFISH implementation) (Moffitt et al , ) that allowed us to accurately measure the expression of 150 genes in ~ 5000 single cells. Since expression covariance between genes from the same pathway is higher compared to genes that have distinct functions (Sigal et al , ; Stewart‐Ornstein et al , ), we focused on a single signaling network and biological function, Ca 2+ response to ATP in epithelial cells, and a biological response important to wound healing (Funaki et al , ; Handly et al , ; Handly & Wollman, ). The key advantage of Ca 2+ response is that the overall signaling response can be measured in < 15 min, a fast timescale that precludes any ATP‐induced changes in transcription.…”

Section: Introductionmentioning

confidence: 99%

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Mammalian gene expression variability is explained by underlying cell state

Foreman

Wollman

2020

Molecular Systems Biology

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120

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Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele‐specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Mammalian gene expression variability is explained by underlying cell state

Foreman

Wollman

2020

Molecular Systems Biology

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120

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“…Given that the genes we probe are related to Ca 2+ signaling we first extracted key features from time-series of cytoplasmic Ca 2+ response measured with a calibrated GCaMP5 biosensor (S2 A). The live cell imaging of cytoplasmic Ca 2+ levels ( Figure 2C) showed a highly heterogeneous response, qualitatively and quantitatively similar to previous work on Ca 2+ signaling in MCF10A cells where we observed a mixed population response with a wide range of response phenotypes (Handly and Wollman, 2017;Yao et al, 2016) . We used a feature-based representation of Ca 2+ response to represent cellular factors that we anticipate correlate with underlying cell state ( Figure 2G and S2).…”

Section: Resultssupporting

confidence: 87%

“…We used sequential hybridization smFISH (MERFISH implementation) ) that allowed us to accurately measure the expression of 150 genes in ~5000 single-cells. Since expression covariance between genes from the same pathway is higher compared to genes that have distinct functions (Sigal et al, 2006;Stewart-Ornstein et al, 2012) , we focused on a single signaling network and biological function, Ca 2+ response to ATP in epithelial cells, a biological response important to wound healing (Funaki et al, 2011;Handly et al, 2015;Handly and Wollman, 2017) .The key advantage of Ca 2+ response is that the overall signaling response can be measured in less than fifteen minutes, a fast timescale that precludes any ATP induced changes in transcription. Using the combined dataset we were able to separate the correlated and uncorrelated components using a simple multiple linear regression model guided by the changes in the covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Mammalian gene expression variability is explained by underlying cell state

Foreman

Wollman

2019

Preprint

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Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH) we measured the multivariate gene expression distribution of 150 genes related to Ca 2+ signaling coupled with the dynamic Ca 2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca 2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

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“…Simultaneous treatment of ionomycin and the potent phosphatase inhibitor Okadaic acid, which inhibits protein phosphatase 1 upstream of YAP (39), led to a similar degree of cytoplasmic sequestration but with minimal recovery, suggesting that Ca² + drives phosphorylation of YAP followed by PP1-dependent dephosphorylation ( Supplementary Figure 2a). Previous reports have shown that the release of extracellular ATP may drive FCW at epithelial wound edge (40). Although addition of 10mM extracellular ATP induced a similar response as TG and ionomycin, we found there was a sharp dependence on local cellular density, with sparse cells responding minimally ( Supplementary Figure 2b).…”

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confidence: 54%

Concerted localization-resets precede YAP-dependent transcription

Ghosh

Franklin

Shi

et al. 2019

Preprint

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Yes-associated protein 1 (YAP) is a transcriptional regulator with critical roles in mechanotransduction, organ size control, and regeneration. Here, we report a robust experimental platform for real-time visualization of native YAP dynamics and target gene expression. Using this platform, we show that activation of YAP target genes is preceded by concerted localization resets, which are dramatic, concerted departure/reentry cycles of nuclear YAP. These resets could be induced by calcium signaling, acto-myosin contractility, and mitotic-exit, and were strictly correlated with YAP-dependent transcription measured using nascent-transcription reporter knockins of YAP target genes. Oncogenically transformed cells with chronically elevated YAP-driven transcription lacked resets but rapidly exchanged YAP between the nucleus and cytoplasm, suggesting an escape from compartmentalization-based control. The single-cell YAP localization and transcription traces suggest a new mode of transcriptional regulation involving the concerted re-partitioning of YAP prior to gene activation. Results and DiscussionThe YAP (YES-associated protein) / TAZ (transcriptional coactivator with PDZ-binding motif) duo(1), a central node of the Hippo pathway (2)(3), controls organ size, and is critical for mechanotransduction (4)(5)(6). The classic view of YAP signal transduction equates nuclear enrichment with activation of pro-growth transcriptional programs through association with the TEAD family of transcription factors (7) (8) (9). However, other critical signal transducers such as ERK, NfkB and P53 use a rich signal transmission code in which the amplitude, frequency, and duration of their nucleocytoplasmic shuttling all influence gene transcription(10)(11)(12). Given recent reports that both YAP and TEAD can alter subcellular localization(13)(14)(15)(16), we speculated that their subcellular spatiotemporal dynamics may encode upstream signaling information. More generally, we should not expect biological signal transmission circuits to be simple, e.g. a linear mapping between an external input onto downstream gene expression level. There are now many examples of gene circuits with hysteresis, memory, and latching (17)(18)(19)(20)(21). Since these circuits involve spatially-controlled components such as transcription factors, it is natural to wonder about the interplay of cellular spatial dynamics and signal processing.To investigate these ideas, we sought to quantify the localization dynamics of native YAP/TEAD in single cells and relate such dynamics to downstream transcription. We used CRISPR(22) to fluorescently tag native YAP and TEAD in breast epithelial cell lines commonly used for studying hippo signaling(23) (Methods, Figure 1a). Since transcription is pulsatile(24)(25), relating localization dynamics to target gene activity requires real-time measurement of gene transcription. We therefore used CRISPR to tag the native mRNAs of two classic YAP/TEAD targets, ANKRD1 (26) and AREG (27), with 24x-MS2 transcriptional reporters.A recen...

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Wound-induced Ca²⁺wave propagates through a simple release and diffusion mechanism

Cited by 37 publications

References 47 publications

Mammalian gene expression variability is explained by underlying cell state

Mammalian gene expression variability is explained by underlying cell state

Mammalian gene expression variability is explained by underlying cell state

Concerted localization-resets precede YAP-dependent transcription

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Wound-induced Ca2+wave propagates through a simple release and diffusion mechanism

Cited by 37 publications

References 47 publications

Mammalian gene expression variability is explained by underlying cell state

Mammalian gene expression variability is explained by underlying cell state

Mammalian gene expression variability is explained by underlying cell state

Concerted localization-resets precede YAP-dependent transcription

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Wound-induced Ca²⁺wave propagates through a simple release and diffusion mechanism