Although biochemical regulation has been extensively studied in organoid modeling protocols, the role of mechanoregulation in directing stem cell fate and organoid development has been relatively unexplored. To accurately replicate the dynamic organoid development observed in nature, in this study, we present a method of heterogeneous embedding using an alginate-shell-Matrigel-core system. This approach allows for cell-Matrigel remodeling by the inner layer and provides short-term moderate-normal compression through the soft alginate outer layer. Our results show that the time-limited confinement contributes to increased expression of neuronal markers such as neurofilament (NF) and microtubule-associated protein 2 (MAP2). Compared with non-alginate embedding and alginate compression groups, volume growth remains unimpeded. Our findings demonstrate the temporary mechanical regulation of cerebral organoid growth, which exhibits a regular growth profile with enhanced maturation. These results highlight the importance and potential practical applications of mechanoregulation in the establishment of brain organoids. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Volumetric compression by heterogeneous scaffold embedding promotes cerebral organoid maturation and does not impede growth.","authors":"Xiaowei Tang, Zitian Wang, Davit Khutsishvili, Yifan Cheng, Jiaqi Wang, Jiyuan Tang, Shaohua Ma","doi":"10.1016/j.cels.2023.09.004","DOIUrl":"10.1016/j.cels.2023.09.004","url":null,"abstract":"<p><p>Although biochemical regulation has been extensively studied in organoid modeling protocols, the role of mechanoregulation in directing stem cell fate and organoid development has been relatively unexplored. To accurately replicate the dynamic organoid development observed in nature, in this study, we present a method of heterogeneous embedding using an alginate-shell-Matrigel-core system. This approach allows for cell-Matrigel remodeling by the inner layer and provides short-term moderate-normal compression through the soft alginate outer layer. Our results show that the time-limited confinement contributes to increased expression of neuronal markers such as neurofilament (NF) and microtubule-associated protein 2 (MAP2). Compared with non-alginate embedding and alginate compression groups, volume growth remains unimpeded. Our findings demonstrate the temporary mechanical regulation of cerebral organoid growth, which exhibits a regular growth profile with enhanced maturation. These results highlight the importance and potential practical applications of mechanoregulation in the establishment of brain organoids. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1016/j.cels.2023.09.005
Evgenia Ntini, Stefan Budach, Ulf A Vang Ørom, Annalisa Marsico
Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.
{"title":"Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs.","authors":"Evgenia Ntini, Stefan Budach, Ulf A Vang Ørom, Annalisa Marsico","doi":"10.1016/j.cels.2023.09.005","DOIUrl":"10.1016/j.cels.2023.09.005","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1016/j.cels.2023.08.002
Alexandra Sockell, Wing Wong, Scott Longwell, Thy Vu, Kasper Karlsson, Daniel Mokhtari, Julia Schaepe, Yuan-Hung Lo, Vincent Cornelius, Calvin Kuo, David Van Valen, Christina Curtis, Polly M Fordyce
Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids.","authors":"Alexandra Sockell, Wing Wong, Scott Longwell, Thy Vu, Kasper Karlsson, Daniel Mokhtari, Julia Schaepe, Yuan-Hung Lo, Vincent Cornelius, Calvin Kuo, David Van Valen, Christina Curtis, Polly M Fordyce","doi":"10.1016/j.cels.2023.08.002","DOIUrl":"10.1016/j.cels.2023.08.002","url":null,"abstract":"<p><p>Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41108086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1016/j.cels.2023.07.002
Shu Wang, Eduardo D Sontag, Douglas A Lauffenburger
A common strategy for exploring single-cell 'omics data is visualizing 2D nonlinear projections that aim to preserve high-dimensional data properties such as neighborhoods. Alternatively, mathematical theory and other computational tools can directly describe data geometry, while also showing that neighborhoods and other properties cannot be well-preserved in any 2D projection.
{"title":"What cannot be seen correctly in 2D visualizations of single-cell 'omics data?","authors":"Shu Wang, Eduardo D Sontag, Douglas A Lauffenburger","doi":"10.1016/j.cels.2023.07.002","DOIUrl":"10.1016/j.cels.2023.07.002","url":null,"abstract":"<p><p>A common strategy for exploring single-cell 'omics data is visualizing 2D nonlinear projections that aim to preserve high-dimensional data properties such as neighborhoods. Alternatively, mathematical theory and other computational tools can directly describe data geometry, while also showing that neighborhoods and other properties cannot be well-preserved in any 2D projection.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10863674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-18Epub Date: 2022-04-13DOI: 10.1016/j.cels.2022.03.001
Christina J Su, Arvind Murugan, James M Linton, Akshay Yeluri, Justin Bois, Heidi Klumpe, Matthew A Langley, Yaron E Antebi, Michael B Elowitz
In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, defined by their receptor expression profiles. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capability. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increases in the number of receptor variants, and is maximized by specific biochemical parameter relationships. Together, these results identify design principles governing cellular addressing by ligand combinations.
{"title":"Ligand-receptor promiscuity enables cellular addressing.","authors":"Christina J Su, Arvind Murugan, James M Linton, Akshay Yeluri, Justin Bois, Heidi Klumpe, Matthew A Langley, Yaron E Antebi, Michael B Elowitz","doi":"10.1016/j.cels.2022.03.001","DOIUrl":"10.1016/j.cels.2022.03.001","url":null,"abstract":"<p><p>In multicellular organisms, secreted ligands selectively activate, or \"address,\" specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, defined by their receptor expression profiles. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capability. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increases in the number of receptor variants, and is maximized by specific biochemical parameter relationships. Together, these results identify design principles governing cellular addressing by ligand combinations.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10897978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139699134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-27Epub Date: 2019-11-06DOI: 10.1016/j.cels.2019.10.001
Benjamin D Knapp, Pascal Odermatt, Enrique R Rojas, Wenpeng Cheng, Xiangwei He, Kerwyn Casey Huang, Fred Chang
Cell growth is a complex process in which cells synthesize cellular components while they increase in size. It is generally assumed that the rate of biosynthesis must somehow be coordinated with the rate of growth in order to maintain intracellular concentrations. However, little is known about potential feedback mechanisms that could achieve proteome homeostasis or the consequences when this homeostasis is perturbed. Here, we identify conditions in which fission yeast cells are prevented from volume expansion but nevertheless continue to synthesize biomass, leading to general accumulation of proteins and increased cytoplasmic density. Upon removal of these perturbations, this biomass accumulation drove cells to undergo a multi-generational period of "supergrowth" wherein rapid volume growth outpaced biosynthesis, returning proteome concentrations back to normal within hours. These findings demonstrate a mechanism for global proteome homeostasis based on modulation of volume growth and dilution.
{"title":"Decoupling of Rates of Protein Synthesis from Cell Expansion Leads to Supergrowth.","authors":"Benjamin D Knapp, Pascal Odermatt, Enrique R Rojas, Wenpeng Cheng, Xiangwei He, Kerwyn Casey Huang, Fred Chang","doi":"10.1016/j.cels.2019.10.001","DOIUrl":"https://doi.org/10.1016/j.cels.2019.10.001","url":null,"abstract":"<p><p>Cell growth is a complex process in which cells synthesize cellular components while they increase in size. It is generally assumed that the rate of biosynthesis must somehow be coordinated with the rate of growth in order to maintain intracellular concentrations. However, little is known about potential feedback mechanisms that could achieve proteome homeostasis or the consequences when this homeostasis is perturbed. Here, we identify conditions in which fission yeast cells are prevented from volume expansion but nevertheless continue to synthesize biomass, leading to general accumulation of proteins and increased cytoplasmic density. Upon removal of these perturbations, this biomass accumulation drove cells to undergo a multi-generational period of \"supergrowth\" wherein rapid volume growth outpaced biosynthesis, returning proteome concentrations back to normal within hours. These findings demonstrate a mechanism for global proteome homeostasis based on modulation of volume growth and dilution.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cels.2019.10.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-23DOI: 10.1016/j.cels.2019.08.003
Nils Eling, Arianne C Richard, Sylvia Richardson, John C Marioni, Catalina A Vallejos
{"title":"Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.","authors":"Nils Eling, Arianne C Richard, Sylvia Richardson, John C Marioni, Catalina A Vallejos","doi":"10.1016/j.cels.2019.08.003","DOIUrl":"https://doi.org/10.1016/j.cels.2019.08.003","url":null,"abstract":"","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-25Epub Date: 2019-09-04DOI: 10.1016/j.cels.2019.06.008
Ryan H Hsu, Ryan L Clark, Jin Wen Tan, John C Ahn, Sonali Gupta, Philip A Romero, Ophelia S Venturelli
Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.
{"title":"Microbial Interaction Network Inference in Microfluidic Droplets.","authors":"Ryan H Hsu, Ryan L Clark, Jin Wen Tan, John C Ahn, Sonali Gupta, Philip A Romero, Ophelia S Venturelli","doi":"10.1016/j.cels.2019.06.008","DOIUrl":"10.1016/j.cels.2019.06.008","url":null,"abstract":"<p><p>Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763379/pdf/nihms-1537657.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-28DOI: 10.1016/j.cels.2019.07.003
Yasir Suhail, Margo P Cain, Kiran Vanaja, Paul A Kurywchak, Andre Levchenko, Raghu Kalluri, Kshitiz
Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.
{"title":"Systems Biology of Cancer Metastasis.","authors":"Yasir Suhail, Margo P Cain, Kiran Vanaja, Paul A Kurywchak, Andre Levchenko, Raghu Kalluri, Kshitiz","doi":"10.1016/j.cels.2019.07.003","DOIUrl":"10.1016/j.cels.2019.07.003","url":null,"abstract":"<p><p>Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6716621/pdf/nihms-1041614.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}