Pub Date : 2024-02-21Epub Date: 2024-02-09DOI: 10.1016/j.cels.2024.01.003
Ryan M Otto, Agata Turska-Nowak, Philip M Brown, Kimberly A Reynolds
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.","authors":"Ryan M Otto, Agata Turska-Nowak, Philip M Brown, Kimberly A Reynolds","doi":"10.1016/j.cels.2024.01.003","DOIUrl":"10.1016/j.cels.2024.01.003","url":null,"abstract":"<p><p>Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. 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":"134-148.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10885703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139716697","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}
Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Single-cell colocalization analysis using a deep generative model.","authors":"Yasuhiro Kojima, Shinji Mii, Shuto Hayashi, Haruka Hirose, Masato Ishikawa, Masashi Akiyama, Atsushi Enomoto, Teppei Shimamura","doi":"10.1016/j.cels.2024.01.007","DOIUrl":"10.1016/j.cels.2024.01.007","url":null,"abstract":"<p><p>Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. 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 2","pages":"180-192.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934738","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 : 2024-02-21Epub Date: 2024-02-09DOI: 10.1016/j.cels.2024.01.002
Hoi Yee Chu, John H C Fong, Dawn G L Thean, Peng Zhou, Frederic K C Fung, Yuanhua Huang, Alan S L Wong
A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Toward this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting with only a few predicted top variants. We find that four rounds of low-N pick-and-validate sampling of 12 variants for machine learning yielded the best accuracy of up to 92.6% in selecting the true top 1% variants in combinatorial mutant libraries, whereas two rounds of 24 variants can also be used. We demonstrate our strategy in successfully discovering high-performance protein variants from diverse families including the CRISPR-based genome editors, supporting its generalizable application for solving protein engineering tasks. A record of this paper's transparent peer review process is included in the supplemental information.
一种能在广阔的组合突变景观中以最少的实验工作量获得最佳变体数量的策略,对于提高蛋白质工程的资源可生产性将大有裨益。为了实现这一目标,我们提出了一种简单有效的基于机器学习的策略,其效果优于其他最先进的方法。我们的策略整合了零次预测和多轮采样,通过仅对少数预测的顶级变异进行实验来指导主动学习。我们发现,通过对 12 个变体进行四轮低 N 挑选和验证采样来进行机器学习,在组合突变体库中选出真正的前 1%变体时,准确率最高可达 92.6%,而对 24 个变体进行两轮采样也是可行的。我们展示了我们的策略,它成功地从包括基于CRISPR的基因组编辑器在内的不同家族中发现了高性能蛋白质变体,支持了它在解决蛋白质工程任务中的可推广应用。本文透明的同行评审过程记录包含在补充信息中。
{"title":"Accurate top protein variant discovery via low-N pick-and-validate machine learning.","authors":"Hoi Yee Chu, John H C Fong, Dawn G L Thean, Peng Zhou, Frederic K C Fung, Yuanhua Huang, Alan S L Wong","doi":"10.1016/j.cels.2024.01.002","DOIUrl":"10.1016/j.cels.2024.01.002","url":null,"abstract":"<p><p>A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Toward this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting with only a few predicted top variants. We find that four rounds of low-N pick-and-validate sampling of 12 variants for machine learning yielded the best accuracy of up to 92.6% in selecting the true top 1% variants in combinatorial mutant libraries, whereas two rounds of 24 variants can also be used. We demonstrate our strategy in successfully discovering high-performance protein variants from diverse families including the CRISPR-based genome editors, supporting its generalizable application for solving protein engineering tasks. 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":"193-203.e6"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139716744","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 : 2024-02-21Epub Date: 2024-02-09DOI: 10.1016/j.cels.2024.01.004
Jeff E Mold, Martin H Weissman, Michael Ratz, Michael Hagemann-Jensen, Joanna Hård, Carl-Johan Eriksson, Hosein Toosi, Joseph Berghenstråhle, Christoph Ziegenhain, Leonie von Berlin, Marcel Martin, Kim Blom, Jens Lagergren, Joakim Lundeberg, Rickard Sandberg, Jakob Michaëlsson, Jonas Frisén
Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Clonally heritable gene expression imparts a layer of diversity within cell types.","authors":"Jeff E Mold, Martin H Weissman, Michael Ratz, Michael Hagemann-Jensen, Joanna Hård, Carl-Johan Eriksson, Hosein Toosi, Joseph Berghenstråhle, Christoph Ziegenhain, Leonie von Berlin, Marcel Martin, Kim Blom, Jens Lagergren, Joakim Lundeberg, Rickard Sandberg, Jakob Michaëlsson, Jonas Frisén","doi":"10.1016/j.cels.2024.01.004","DOIUrl":"10.1016/j.cels.2024.01.004","url":null,"abstract":"<p><p>Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. 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":"149-165.e10"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139716745","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 : 2024-01-17DOI: 10.1016/j.cels.2023.12.008
Joseph M Heili, Kaitlin Stokes, Nathaniel J Gaut, Christopher Deich, Judee Sharon, Tanner Hoog, Jose Gomez-Garcia, Brock Cash, Matthew R Pawlak, Aaron E Engelhart, Katarzyna P Adamala
Synthetic minimal cells are a class of bioreactors that have some, but not all, functions of live cells. Here, we report a critical step toward the development of a bottom-up minimal cell: cellular export of functional protein and RNA products. We used cell-penetrating peptide tags to translocate payloads across a synthetic cell vesicle membrane. We demonstrated efficient transport of active enzymes and transport of nucleic acid payloads by RNA-binding proteins. We investigated influence of a concentration gradient alongside other factors on the efficiency of the translocation, and we show a method to increase product accumulation in one location. We demonstrate the use of this technology to engineer molecular communication between different populations of synthetic cells, to exchange protein and nucleic acid signals. The synthetic minimal cell production and export of proteins or nucleic acids allows experimental designs that approach the complexity and relevancy of natural biological systems. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Controlled exchange of protein and nucleic acid signals from and between synthetic minimal cells.","authors":"Joseph M Heili, Kaitlin Stokes, Nathaniel J Gaut, Christopher Deich, Judee Sharon, Tanner Hoog, Jose Gomez-Garcia, Brock Cash, Matthew R Pawlak, Aaron E Engelhart, Katarzyna P Adamala","doi":"10.1016/j.cels.2023.12.008","DOIUrl":"10.1016/j.cels.2023.12.008","url":null,"abstract":"<p><p>Synthetic minimal cells are a class of bioreactors that have some, but not all, functions of live cells. Here, we report a critical step toward the development of a bottom-up minimal cell: cellular export of functional protein and RNA products. We used cell-penetrating peptide tags to translocate payloads across a synthetic cell vesicle membrane. We demonstrated efficient transport of active enzymes and transport of nucleic acid payloads by RNA-binding proteins. We investigated influence of a concentration gradient alongside other factors on the efficiency of the translocation, and we show a method to increase product accumulation in one location. We demonstrate the use of this technology to engineer molecular communication between different populations of synthetic cells, to exchange protein and nucleic acid signals. The synthetic minimal cell production and export of proteins or nucleic acids allows experimental designs that approach the complexity and relevancy of natural biological systems. 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 1","pages":"49-62.e4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139492937","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 : 2024-01-17DOI: 10.1016/j.cels.2023.12.007
Anna C Kögler, Patrick Müller
Signaling pathways feature multiple interacting ligand and receptor variants, which are thought to act in a combinatorial manner to elicit different cellular responses. Transcriptome analyses now suggest that many signaling pathways use their components in combinations that are surprisingly often shared between otherwise dissimilar cell states.
{"title":"Modes and motifs in multicellular communication.","authors":"Anna C Kögler, Patrick Müller","doi":"10.1016/j.cels.2023.12.007","DOIUrl":"10.1016/j.cels.2023.12.007","url":null,"abstract":"<p><p>Signaling pathways feature multiple interacting ligand and receptor variants, which are thought to act in a combinatorial manner to elicit different cellular responses. Transcriptome analyses now suggest that many signaling pathways use their components in combinations that are surprisingly often shared between otherwise dissimilar cell states.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139492942","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 : 2024-01-17Epub Date: 2023-12-20DOI: 10.1016/j.cels.2023.12.002
Gaurav Jumde, Bastiaan Spanjaard, Jan Philipp Junker
Stem cells differentiate into distinct fates by transitioning through a series of transcriptional states. Current computational approaches allow reconstruction of differentiation trajectories from single-cell transcriptomics data, but it remains unknown to what degree differentiation can be predicted across biological processes. Here, we use transfer learning to infer differentiation processes and quantify predictability in early embryonic development and adult hematopoiesis. Overall, we find that non-linear methods outperform linear approaches, and we achieved the best predictions with a custom variational autoencoder that explicitly models changes in transcriptional variance. We observed a high accuracy of predictions in embryonic development, but we found somewhat lower agreement with the real data in adult hematopoiesis. We demonstrate that this discrepancy can be explained by a higher degree of concordant transcriptional processes along embryonic differentiation compared with adult homeostasis. In summary, we establish a framework for quantifying and exploiting predictability of cellular differentiation trajectories.
{"title":"Inference of differentiation trajectories by transfer learning across biological processes.","authors":"Gaurav Jumde, Bastiaan Spanjaard, Jan Philipp Junker","doi":"10.1016/j.cels.2023.12.002","DOIUrl":"10.1016/j.cels.2023.12.002","url":null,"abstract":"<p><p>Stem cells differentiate into distinct fates by transitioning through a series of transcriptional states. Current computational approaches allow reconstruction of differentiation trajectories from single-cell transcriptomics data, but it remains unknown to what degree differentiation can be predicted across biological processes. Here, we use transfer learning to infer differentiation processes and quantify predictability in early embryonic development and adult hematopoiesis. Overall, we find that non-linear methods outperform linear approaches, and we achieved the best predictions with a custom variational autoencoder that explicitly models changes in transcriptional variance. We observed a high accuracy of predictions in embryonic development, but we found somewhat lower agreement with the real data in adult hematopoiesis. We demonstrate that this discrepancy can be explained by a higher degree of concordant transcriptional processes along embryonic differentiation compared with adult homeostasis. In summary, we establish a framework for quantifying and exploiting predictability of cellular differentiation trajectories.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"75-82.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833525","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}
In microbial systems, a metabolic pathway can be either completed by one autonomous population or distributed among a consortium performing metabolic division of labor (MDOL). MDOL facilitates the system's function by reducing the metabolic burden; however, it may hinder the function by reducing the exchange efficiency of metabolic intermediates among individuals. As a result, the function of a community is influenced by the trade-offs between the metabolic specialization and versatility of individuals. To experimentally test this hypothesis, we deconstructed the naphthalene degradation pathway into four steps and introduced them individually or combinatorically into different strains with varying levels of metabolic specialization. Using these strains, we engineered 1,456 synthetic consortia and found that 74 consortia exhibited higher degradation function than both the autonomous population and rigorous MDOL consortium. Quantitative modeling provides general strategies for identifying the most effective MDOL configuration. Our study provides critical insights into the engineering of high-performance microbial systems.
{"title":"The trade-off between individual metabolic specialization and versatility determines the metabolic efficiency of microbial communities.","authors":"Miaoxiao Wang, Xiaoli Chen, Yuan Fang, Xin Zheng, Ting Huang, Yong Nie, Xiao-Lei Wu","doi":"10.1016/j.cels.2023.12.004","DOIUrl":"10.1016/j.cels.2023.12.004","url":null,"abstract":"<p><p>In microbial systems, a metabolic pathway can be either completed by one autonomous population or distributed among a consortium performing metabolic division of labor (MDOL). MDOL facilitates the system's function by reducing the metabolic burden; however, it may hinder the function by reducing the exchange efficiency of metabolic intermediates among individuals. As a result, the function of a community is influenced by the trade-offs between the metabolic specialization and versatility of individuals. To experimentally test this hypothesis, we deconstructed the naphthalene degradation pathway into four steps and introduced them individually or combinatorically into different strains with varying levels of metabolic specialization. Using these strains, we engineered 1,456 synthetic consortia and found that 74 consortia exhibited higher degradation function than both the autonomous population and rigorous MDOL consortium. Quantitative modeling provides general strategies for identifying the most effective MDOL configuration. Our study provides critical insights into the engineering of high-performance microbial systems.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 1","pages":"63-74.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139492944","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 : 2024-01-17Epub Date: 2024-01-08DOI: 10.1016/j.cels.2023.12.003
Mason Minot, Sai T Reddy
Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.
{"title":"Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering.","authors":"Mason Minot, Sai T Reddy","doi":"10.1016/j.cels.2023.12.003","DOIUrl":"10.1016/j.cels.2023.12.003","url":null,"abstract":"<p><p>Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"4-18.e4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405650","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 : 2023-12-20DOI: 10.1016/j.cels.2023.11.002
Cameron D Vasquez, John G Albeck
Single-cell data and computational simulations reveal the dynamics of the transcription factors HIF1α and PPARγ during adipocyte differentiation and maturation. Modeling feedback within this network predicts a HIF1α-mediated choice between lipid accumulation and incomplete differentiation. In vitro experiments support this model, with implications for adipose dynamics in metabolic disorders involving hypoxia.
{"title":"Modeling elucidates context dependence in adipose regulation.","authors":"Cameron D Vasquez, John G Albeck","doi":"10.1016/j.cels.2023.11.002","DOIUrl":"10.1016/j.cels.2023.11.002","url":null,"abstract":"<p><p>Single-cell data and computational simulations reveal the dynamics of the transcription factors HIF1α and PPARγ during adipocyte differentiation and maturation. Modeling feedback within this network predicts a HIF1α-mediated choice between lipid accumulation and incomplete differentiation. In vitro experiments support this model, with implications for adipose dynamics in metabolic disorders involving hypoxia.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"14 12","pages":"1021-1023"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833528","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}