Cell systems最新文献

Asymmetric crosstalk between the BMP and TGF-β pathways resolves signaling ambiguity. BMP和TGF-β信号通路之间的不对称串扰解决了信号的模糊性。

IF 7.7

Cell systems Pub Date : 2026-10-20 DOI: 10.1016/j.cels.2026.101757

Johannes M Auth, Inbal Eizenberg-Magar, Omer Erez Dekel, Omer Zachar, Merav D Shmueli, Inbal Zigdon, Achinoam Shoham, Vladimir Mindel, Tsviya Olender, Meytar Asulin Berrebi, Ariel Tennenhouse, Neta Bar-Hai, Rakefet Ben-Yishay, Dana Ishay-Ronen, Yaron E Antebi

{"title":"Asymmetric crosstalk between the BMP and TGF-β pathways resolves signaling ambiguity.","authors":"Johannes M Auth, Inbal Eizenberg-Magar, Omer Erez Dekel, Omer Zachar, Merav D Shmueli, Inbal Zigdon, Achinoam Shoham, Vladimir Mindel, Tsviya Olender, Meytar Asulin Berrebi, Ariel Tennenhouse, Neta Bar-Hai, Rakefet Ben-Yishay, Dana Ishay-Ronen, Yaron E Antebi","doi":"10.1016/j.cels.2026.101757","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101757","url":null,"abstract":"The BMP and TGF-β signaling pathways control cellular fate decisions in diverse biological contexts, often playing opposing roles. Despite extensive knowledge of these pathways, understanding how cells respond to environments containing these opposing cues remains a challenge. Here, we systematically analyze the activation of these pathways under combinatorial signaling environments. We find that TGF-β ligands inhibit BMP signaling while BMP ligands enhance TGF-β transcriptional response across concentrations, ligand variants, and cell types. This asymmetric crosstalk results in the activation of a TGF-β-biased transcriptional response even under mixed-signaling conditions, effectively reducing signal ambiguity, with implications for processes such as epithelial-to-mesenchymal transition (EMT). We show that this crosstalk originates downstream of SMAD protein phosphorylation. Using mathematical models, we predict and experimentally verify that promiscuous interactions between SMAD proteins provide the mechanism for the observed crosstalk. Our findings challenge the canonical models, suggesting an active role for mediator proteins in determining biological responses.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101757"},"PeriodicalIF":7.7,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229534","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}

引用次数: 0

CAR T cells from code to clinic: Framing modeling approaches with current translational research goals. CAR - T细胞从代码到临床：框架建模方法与当前的转化研究目标。

IF 7.7

Cell systems Pub Date : 2026-10-14 DOI: 10.1016/j.cels.2026.101716

Lucas E Sant'Anna, Rohita Roy, Janella C Schwab, Julian I Perez, Michaëlle N Mayalu

引用次数: 0

Systematic transcriptomic evaluation of blastoid models of early human development. 早期人类发育囊胚模型的系统转录组学评价。

IF 7.7

Cell systems Pub Date : 2026-10-02 DOI: 10.1016/j.cels.2026.101738

Siqu Long, Hani Jieun Kim, Nazmus Salehin, Hao Huang, Xinran Zhang, Raja Jothi, Pengyi Yang

引用次数: 0

FLIGHTED: Inferring fitness landscapes from noisy high-throughput experimental data. flighting：从嘈杂的高通量实验数据推断健身景观。

IF 7.7

Cell systems Pub Date : 2026-10-02 DOI: 10.1016/j.cels.2026.101739

Vikram Sundar, Boqiang Tu, Lindsey Guan, Kevin Esvelt

{"title":"FLIGHTED: Inferring fitness landscapes from noisy high-throughput experimental data.","authors":"Vikram Sundar, Boqiang Tu, Lindsey Guan, Kevin Esvelt","doi":"10.1016/j.cels.2026.101739","DOIUrl":"10.1016/j.cels.2026.101739","url":null,"abstract":"Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, thereby harming model performance. Here, we develop fitness landscape inference generated by high-throughput experimental data (FLIGHTED), a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy experiments. We demonstrate how FLIGHTED can improve model performance on two experiments: single-step selection assays, such as phage display, and a high-throughput assay that ties activity to base editing. We compare the performance of standard ML models on fitness landscapes with and without FLIGHTED. Accounting for noise statistically significantly improves model performance. Based on our new benchmarking with FLIGHTED, data size, not model scale, is limiting protein fitness model performance. Our results indicate that FLIGHTED can be applied to any high-throughput assay and any ML model, making it straightforward for protein designers to account for noise.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101739"},"PeriodicalIF":7.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120109","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}

引用次数: 0

CRISPR-associated enzymes are mislocalized to the cytoplasm in iPSC-derived neurons, resulting in KRAB(KOX1)-specific degradation. 在ipsc衍生的神经元中，crispr相关酶错误定位到细胞质上，导致KRAB（KOX1）特异性降解。

IF 7.7

Cell systems Pub Date : 2026-09-23 DOI: 10.1016/j.cels.2026.101737

Gregory Cajka, Nima N Naseri, Matthew H Liu, Ophir Shalem

{"title":"CRISPR-associated enzymes are mislocalized to the cytoplasm in iPSC-derived neurons, resulting in KRAB(KOX1)-specific degradation.","authors":"Gregory Cajka, Nima N Naseri, Matthew H Liu, Ophir Shalem","doi":"10.1016/j.cels.2026.101737","DOIUrl":"10.1016/j.cels.2026.101737","url":null,"abstract":"The use of CRISPR-associated enzymes in induced pluripotent stem cell (iPSC)-derived neurons presents unique challenges compared with dividing cell lines. For example, loss of dCas9-KRAB expression after differentiation has been observed and largely ascribed to transgene silencing. Here, we investigated the expression of CRISPR enzymes in iPSCs and Ngn2-derived neurons. We found that the commonly used dCas9-KRAB(KOX1) displayed a dramatic reduction in protein levels following differentiation, yet nCas9 constructs retained comparable levels. We further found that CRISPR constructs, primarily relying on the SV40 nuclear localization signal (NLS), fail to localize to the nuclei of neurons, despite having robust nuclear levels in iPSCs, leading to KRAB(KOX1)-specific cytoplasmic degradation. By testing other NLSs, we rescued neuronal nuclear localization and protein expression, confirming the contribution of mislocalization to the instability of dCas9-KRAB(KOX1) in neurons. As the lack of nuclear localization can have a profound impact on CRISPR activity, we suggest further investigation across cultured and in vivo postmitotic cell models. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101737"},"PeriodicalIF":7.7,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032238","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}

引用次数: 0

The drifting biochemical basis of an essential molecular interaction. 必不可少的分子相互作用的飘忽不定的生化基础。

IF 7.7

Cell systems Pub Date : 2026-09-21 DOI: 10.1016/j.cels.2026.101736

Yeonwoo Park, V Narry Kim

引用次数: 0

Foundation model reveals the shared organization of transcription and topologically associating domains. 基础模型揭示了转录和拓扑关联域的共享组织。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-07-17 DOI: 10.1016/j.cels.2026.101675

Huan Liang, Bonnie Berger, Rohit Singh

{"title":"Foundation model reveals the shared organization of transcription and topologically associating domains.","authors":"Huan Liang, Bonnie Berger, Rohit Singh","doi":"10.1016/j.cels.2026.101675","DOIUrl":"10.1016/j.cels.2026.101675","url":null,"abstract":"The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes into contact. However, studies of TADs' function and their influence on transcription have been constrained by ambiguities in TAD boundary definitions and challenges in directly measuring their regulatory effects. We overcome these limitations by developing species-level consensus TAD maps for human and mouse by using a bag-of-genes approach that exposes an emergent regulatory structure. To quantify TAD-mediated relationships, we use a foundation model trained on 33 million transcriptomes to define a contextual similarity metric that captures higher-order relationships missed by co-expression. We find that TADs are regions of elevated co-regulation, with our framework yielding testable hypotheses about chromatin organization across cellular states. This TAD-linked enhancement is strongest during early development and declines with aging, while cancer cells show distinct TAD usage that shifts with chemotherapy. Together, these findings suggest that chromatin organization acts through probabilistic rather than deterministic mechanisms.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101675"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13418072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474571","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}

引用次数: 0

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding. 转录激活域的可解释生物物理神经网络分离蛋白质丰度和辅激活因子结合的作用。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-08-17 DOI: 10.1016/j.cels.2026.101701

Claire J LeBlanc, Pooja Agarwal, Jackson E Demaray, Gean Hu, Marissa A Zintel, Angelica W Y Lam, Joel Enrique Castro Hernandez, Max V Staller

{"title":"Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.","authors":"Claire J LeBlanc, Pooja Agarwal, Jackson E Demaray, Gean Hu, Marissa A Zintel, Angelica W Y Lam, Joel Enrique Castro Hernandez, Max V Staller","doi":"10.1016/j.cels.2026.101701","DOIUrl":"10.1016/j.cels.2026.101701","url":null,"abstract":"Deep neural networks have improved many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn the molecular mechanisms. Here, we address interpretation challenges by building biophysical neural networks for predicting activation domains, the regions within transcription factors (TFs) that recruit coactivators to drive gene expression. Deep neural networks can now accurately predict acidic activation domains from protein sequences, but these predictors are difficult to interpret. We designed shallow neural networks that incorporated biophysical models and visualized the parameters directly. We found two ways that the arrangement of residues (i.e., sequence grammar) controls function: (1) C-terminal hydrophobic residues increase coactivator binding and decrease protein abundance, and (2) acidic residues at the N terminus promote coactivator binding, while acidic residues at the C terminus promote TF abundance. We demonstrate how combining biophysical and deep neural networks maximizes prediction accuracy and interpretability, revealing biological mechanisms across datasets. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101701"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13596073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803134","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}

引用次数: 0

Evaluating SARS-CoV-2 antibody resilience via prediction and design of escape viral variants. 通过预测和设计逃逸病毒变体评估SARS-CoV-2抗体弹性

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-07-17 DOI: 10.1016/j.cels.2026.101673

Marian Huot, Pierre Rosenbaum, Cyril Planchais, Hugo Mouquet, Rémi Monasson, Simona Cocco

{"title":"Evaluating SARS-CoV-2 antibody resilience via prediction and design of escape viral variants.","authors":"Marian Huot, Pierre Rosenbaum, Cyril Planchais, Hugo Mouquet, Rémi Monasson, Simona Cocco","doi":"10.1016/j.cels.2026.101673","DOIUrl":"10.1016/j.cels.2026.101673","url":null,"abstract":"The evolutionary trajectory of SARS-CoV-2 is shaped by competing pressures for angiotensin-converting enzyme 2 (ACE2) binding, viability, and escape from neutralizing antibodies targeting its receptor-binding domain (RBD). Here, we present EscapeMap, a modular framework that enables the prediction and design of variants escaping antibodies. EscapeMap integrates deep mutational scanning data for ACE2 and 31 monoclonal antibodies with a generative sequence model trained on pre-pandemic Coronaviridae. To experimentally probe escape potential, we designed RBD variants under pressure from four clinically relevant antibodies (SA55, S2E12, S309, and VIR-7229). Among these designs, bearing up to 21 mutations from wild type, 50% expressed as stable proteins. Binding assays confirm that S309 and VIR-7229 retain recognition across diverse mutation combinations. EscapeMap accurately forecasts which antibodies are vulnerable to escape by our designed sequences. Finally, by identifying correlated escape routes, we predict and experimentally verify antibody combinations less prone to simultaneous escape, offering a quantitative basis for guiding therapeutic strategies.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101673"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474602","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}

引用次数: 0

Multiobjective learning and design of bacteriophage specificity. 噬菌体特异性的多目标学习与设计。

IF 7.7

Cell systems Pub Date : 2026-09-16 Epub Date: 2026-09-01 DOI: 10.1016/j.cels.2026.101712

Naia Novy, Phil Huss, Sarah Evert, Philip A Romero, Srivatsan Raman

引用次数: 0