Pub Date : 2024-08-21Epub Date: 2024-08-05DOI: 10.1016/j.cels.2024.07.005
Xinran Lian, Nikša Praljak, Subu K Subramanian, Sarah Wasinger, Rama Ranganathan, Andrew L Ferguson
Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.
{"title":"Deep-learning-based design of synthetic orthologs of SH3 signaling domains.","authors":"Xinran Lian, Nikša Praljak, Subu K Subramanian, Sarah Wasinger, Rama Ranganathan, Andrew L Ferguson","doi":"10.1016/j.cels.2024.07.005","DOIUrl":"10.1016/j.cels.2024.07.005","url":null,"abstract":"<p><p>Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"725-737.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899168","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 : 2024-08-21Epub Date: 2024-08-07DOI: 10.1016/j.cels.2024.07.001
Alexander T F Bell, Jacob T Mitchell, Ashley L Kiemen, Melissa Lyman, Kohei Fujikura, Jae W Lee, Erin Coyne, Sarah M Shin, Sushma Nagaraj, Atul Deshpande, Pei-Hsun Wu, Dimitrios N Sidiropoulos, Rossin Erbe, Jacob Stern, Rena Chan, Stephen Williams, James M Chell, Lauren Ciotti, Jacquelyn W Zimmerman, Denis Wirtz, Won Jin Ho, Neeha Zaidi, Elizabeth Thompson, Elizabeth M Jaffee, Laura D Wood, Elana J Fertig, Luciane T Kagohara
This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
{"title":"PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.","authors":"Alexander T F Bell, Jacob T Mitchell, Ashley L Kiemen, Melissa Lyman, Kohei Fujikura, Jae W Lee, Erin Coyne, Sarah M Shin, Sushma Nagaraj, Atul Deshpande, Pei-Hsun Wu, Dimitrios N Sidiropoulos, Rossin Erbe, Jacob Stern, Rena Chan, Stephen Williams, James M Chell, Lauren Ciotti, Jacquelyn W Zimmerman, Denis Wirtz, Won Jin Ho, Neeha Zaidi, Elizabeth Thompson, Elizabeth M Jaffee, Laura D Wood, Elana J Fertig, Luciane T Kagohara","doi":"10.1016/j.cels.2024.07.001","DOIUrl":"10.1016/j.cels.2024.07.001","url":null,"abstract":"<p><p>This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"753-769.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908694","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 : 2024-08-21Epub Date: 2024-08-08DOI: 10.1016/j.cels.2024.07.002
Dimitris Volteras, Vahid Shahrezaei, Philipp Thomas
Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing.","authors":"Dimitris Volteras, Vahid Shahrezaei, Philipp Thomas","doi":"10.1016/j.cels.2024.07.002","DOIUrl":"10.1016/j.cels.2024.07.002","url":null,"abstract":"<p><p>Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"694-708.e12"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914806","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 : 2024-08-21DOI: 10.1016/j.cels.2024.07.009
Anthony Gitter
One snapshot of the peer review process for "Transcriptome data are insufficient to control false discoveries in regulatory network inference" (Kernfeld et al., 2024).1.
转录组数据不足以控制调控网络推断中的错误发现"(Kernfeld et al.
{"title":"Evaluation of Kernfeld et al.: Toward best practices for tackling false positives in regulatory network inference.","authors":"Anthony Gitter","doi":"10.1016/j.cels.2024.07.009","DOIUrl":"https://doi.org/10.1016/j.cels.2024.07.009","url":null,"abstract":"<p><p>One snapshot of the peer review process for \"Transcriptome data are insufficient to control false discoveries in regulatory network inference\" (Kernfeld et al., 2024).<sup>1</sup>.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 8","pages":"673-675"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142038012","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 : 2024-08-21DOI: 10.1016/j.cels.2024.07.006
Eric Kernfeld, Rebecca Keener, Patrick Cahan, Alexis Battle
Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Transcriptome data are insufficient to control false discoveries in regulatory network inference.","authors":"Eric Kernfeld, Rebecca Keener, Patrick Cahan, Alexis Battle","doi":"10.1016/j.cels.2024.07.006","DOIUrl":"10.1016/j.cels.2024.07.006","url":null,"abstract":"<p><p>Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring \"causal sufficiency,\" meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 8","pages":"709-724.e13"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11642480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142038015","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 : 2024-08-21DOI: 10.1016/j.cels.2024.07.007
Yuting Liu, Zhen Zhou, Hetian Su, Songlin Wu, Gavin Ni, Alex Zhang, Lev S Tsimring, Jeff Hasty, Nan Hao
Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.
{"title":"Enhanced cellular longevity arising from environmental fluctuations.","authors":"Yuting Liu, Zhen Zhou, Hetian Su, Songlin Wu, Gavin Ni, Alex Zhang, Lev S Tsimring, Jeff Hasty, Nan Hao","doi":"10.1016/j.cels.2024.07.007","DOIUrl":"10.1016/j.cels.2024.07.007","url":null,"abstract":"<p><p>Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the \"healthy\" state of the cell and the \"dynamic stabilization\" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 8","pages":"738-752.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142038011","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 : 2024-08-21Epub Date: 2024-08-13DOI: 10.1016/j.cels.2024.07.003
Levente Varga, Vasile V Moca, Botond Molnár, Laura Perez-Cervera, Mohamed Kotb Selim, Antonio Díaz-Parra, David Moratal, Balázs Péntek, Wolfgang H Sommer, Raul C Mureșan, Santiago Canals, Maria Ercsey-Ravasz
Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.
{"title":"Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture.","authors":"Levente Varga, Vasile V Moca, Botond Molnár, Laura Perez-Cervera, Mohamed Kotb Selim, Antonio Díaz-Parra, David Moratal, Balázs Péntek, Wolfgang H Sommer, Raul C Mureșan, Santiago Canals, Maria Ercsey-Ravasz","doi":"10.1016/j.cels.2024.07.003","DOIUrl":"10.1016/j.cels.2024.07.003","url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"770-786.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984113","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 : 2024-07-17Epub Date: 2024-06-11DOI: 10.1016/j.cels.2024.05.007
Niklas F C Hummel, Kasey Markel, Jordan Stefani, Max V Staller, Patrick M Shih
Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.
{"title":"Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast.","authors":"Niklas F C Hummel, Kasey Markel, Jordan Stefani, Max V Staller, Patrick M Shih","doi":"10.1016/j.cels.2024.05.007","DOIUrl":"10.1016/j.cels.2024.05.007","url":null,"abstract":"<p><p>Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, \"universal\" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"662-672.e4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312522","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 : 2024-07-17Epub Date: 2024-07-05DOI: 10.1016/j.cels.2024.06.001
Mengtong Duan, Ishaan Dev, Andrew Lu, Goar Ayrapetyan, Mei Yi You, Mikhail G Shapiro
Here, we present a method for expressing multiple open reading frames (ORFs) from single transcripts using the leaky scanning model of translation initiation. In this approach termed "stoichiometric expression of mRNA polycistrons by eukaryotic ribosomes" (SEMPER), adjacent ORFs are translated from a single mRNA at tunable ratios determined by their order in the sequence and the strength of their translation initiation sites. We validate this approach by expressing up to three fluorescent proteins from one plasmid in two different cell lines. We then use it to encode a stoichiometrically tuned polycistronic construct encoding gas vesicle acoustic reporter genes that enables efficient formation of the multi-protein complex while minimizing cellular toxicity. We also demonstrate that SEMPER enables polycistronic expression of recombinant monoclonal antibodies from plasmid DNA and of two fluorescent proteins from single mRNAs made through in vitro transcription. Finally, we provide a probabilistic model to elucidate the mechanisms underlying SEMPER. A record of this paper's transparent peer review process is included in the supplemental information.
在这里,我们介绍了一种利用翻译起始的漏扫描模式从单个转录本表达多个开放阅读框(ORF)的方法。在这种被称为 "真核核糖体对 mRNA 多单链体的定量表达"(SEMPER)的方法中,相邻的 ORF 以可调节的比例从单个 mRNA 翻译出来,这种比例由它们在序列中的顺序和翻译起始位点的强度决定。我们通过在两种不同的细胞系中用一个质粒表达多达三种荧光蛋白来验证这种方法。然后,我们用它来编码一个按比例调整的多聚组蛋白构建体,该构建体编码气泡声学报告基因,能有效地形成多蛋白复合物,同时将细胞毒性降到最低。我们还证明,SEMPER 可使质粒 DNA 中的重组单克隆抗体和体外转录的单个 mRNA 中的两种荧光蛋白实现多聚体表达。最后,我们提供了一个概率模型来阐明 SEMPER 的内在机制。本文的同行评审过程透明,其记录见补充信息。
{"title":"SEMPER: Stoichiometric expression of mRNA polycistrons by eukaryotic ribosomes for compact, ratio-tunable multi-gene expression.","authors":"Mengtong Duan, Ishaan Dev, Andrew Lu, Goar Ayrapetyan, Mei Yi You, Mikhail G Shapiro","doi":"10.1016/j.cels.2024.06.001","DOIUrl":"10.1016/j.cels.2024.06.001","url":null,"abstract":"<p><p>Here, we present a method for expressing multiple open reading frames (ORFs) from single transcripts using the leaky scanning model of translation initiation. In this approach termed \"stoichiometric expression of mRNA polycistrons by eukaryotic ribosomes\" (SEMPER), adjacent ORFs are translated from a single mRNA at tunable ratios determined by their order in the sequence and the strength of their translation initiation sites. We validate this approach by expressing up to three fluorescent proteins from one plasmid in two different cell lines. We then use it to encode a stoichiometrically tuned polycistronic construct encoding gas vesicle acoustic reporter genes that enables efficient formation of the multi-protein complex while minimizing cellular toxicity. We also demonstrate that SEMPER enables polycistronic expression of recombinant monoclonal antibodies from plasmid DNA and of two fluorescent proteins from single mRNAs made through in vitro transcription. Finally, we provide a probabilistic model to elucidate the mechanisms underlying SEMPER. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"597-609.e4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11298409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545640","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 : 2024-07-17Epub Date: 2024-07-08DOI: 10.1016/j.cels.2024.06.004
Catherine M Porter, Grace C Qian, Samuel H Grindel, Alex J Hughes
Organoids derived from human stem cells are a promising approach for disease modeling, regenerative medicine, and fundamental research. However, organoid variability and limited control over morphological outcomes remain as challenges. One open question is the extent to which engineering control over culture conditions can guide organoids to specific compositions. Here, we extend a DNA "velcro" cell patterning approach, precisely controlling the number and ratio of human induced pluripotent stem cell-derived progenitors contributing to nephron progenitor (NP) organoids and mosaic NP/ureteric bud (UB) tip cell organoids within arrays of microwells. We demonstrate long-term control over organoid size and morphology, decoupled from geometric constraints. We then show emergent trends in organoid tissue proportions that depend on initial progenitor cell composition. These include higher nephron and stromal cell representation in mosaic NP/UB organoids vs. NP-only organoids and a "goldilocks" initial cell ratio in mosaic organoids that optimizes the formation of proximal tubule structures.
{"title":"Highly parallel production of designer organoids by mosaic patterning of progenitors.","authors":"Catherine M Porter, Grace C Qian, Samuel H Grindel, Alex J Hughes","doi":"10.1016/j.cels.2024.06.004","DOIUrl":"10.1016/j.cels.2024.06.004","url":null,"abstract":"<p><p>Organoids derived from human stem cells are a promising approach for disease modeling, regenerative medicine, and fundamental research. However, organoid variability and limited control over morphological outcomes remain as challenges. One open question is the extent to which engineering control over culture conditions can guide organoids to specific compositions. Here, we extend a DNA \"velcro\" cell patterning approach, precisely controlling the number and ratio of human induced pluripotent stem cell-derived progenitors contributing to nephron progenitor (NP) organoids and mosaic NP/ureteric bud (UB) tip cell organoids within arrays of microwells. We demonstrate long-term control over organoid size and morphology, decoupled from geometric constraints. We then show emergent trends in organoid tissue proportions that depend on initial progenitor cell composition. These include higher nephron and stromal cell representation in mosaic NP/UB organoids vs. NP-only organoids and a \"goldilocks\" initial cell ratio in mosaic organoids that optimizes the formation of proximal tubule structures.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"649-661.e9"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565354","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}