Cell systems最新文献

T cells develop from hematopoietic progenitors in the thymus and protect against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors and their subsequent specification to the T lineage have been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single-cell RNA sequencing revealed that downregulation of the multipotent hematopoietic program, upregulation of >90 lineage-associated transcription factors, and cell-cycle exit all occur within a highly coordinated developmental window. Gene-regulatory network inference uncovered a role for YBX1 in T lineage specification. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and discovered that mast and myeloid potential bifurcate from each other early in hematopoiesis, upstream of T lineage restriction. Our systems-level analyses provide a quantitative, time-resolved model of human T cell fate specification. A record of this paper's transparent peer review process is included in the supplemental information.

The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

In recent years, the advances in high-throughput and deep sequencing have generated a diverse amount of adaptive immune repertoire data. This surge in data has seen a proportional increase in computational methods aimed to characterize T cell receptor (TCR) repertoires. In this perspective, we will provide a brief commentary on the various domains of TCR repertoire analysis, their respective computational methods, and the ongoing challenges. Given the breadth of methods and applications of TCR analysis, we will focus our perspective on sequence-based computational methods.

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.

Turing patterns are self-organizing systems that can form spots, stripes, or labyrinths. Proposed examples in tissue organization include zebrafish pigmentation, digit spacing, and many others. The theory of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters. This has complicated the engineering of synthetic Turing gene circuits from first principles, although natural genetic Turing networks have been identified. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness. The system reproducibly generated stationary, periodic, concentric stripe patterns in growing E. coli colonies. A partial differential equation model reproduced the patterns, with a Turing parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to nanotechnologies, such as patterned biomaterial deposition, and provide insights into developmental patterning programs. A record of this paper's transparent peer review process is included in the supplemental information.

The field of antibody discovery typically involves extensive experimental screening of B cells from immunized animals. Machine learning (ML)-guided prediction of antigen-specific B cells could accelerate this process but requires sufficient training data with antigen-specificity labeling. Here, we introduce a dataset of single-cell transcriptome and antibody repertoire sequencing of B cells from immunized mice, which are labeled as antigen specific or non-specific through experimental selections. We identify gene expression patterns associated with antigen specificity by differential gene expression analysis and assess their antibody sequence diversity. Subsequently, we benchmark various ML models, both linear and non-linear, trained on different combinations of gene expression and antibody repertoire features. Additionally, we assess transfer learning using features from general and antibody-specific protein language models (PLMs). Our findings show that gene expression-based models outperform sequence-based models for antigen-specificity predictions, highlighting a promising avenue for computationally guided antibody discovery.

A new study deconvolutes the systems-level control of the cGAS-STING pathway and identifies many novel regulators of STING biology. This was made possible by optical pooled screening (OPS), which enables high-dimensional imaging of millions of gene-edited cells, showcasing the future of CRISPR screening.

Established and emergent microengineered in vitro systems enable the evaluation of chimeric antigen receptor (CAR) T cell product purity, avidity, and functionality. Here, we describe such systems and how they have been used to optimize CAR T cell products by selecting highly viable cells, eliminating off-target cells, and tailoring avidity to balance efficacy and safety. The future of CAR T cell therapy development and manufacturing is expected to be anchored in a cyclical process that integrates multiple high-throughput and patient-centered techniques for identifying, enriching, and evaluating T cell subtypes. This article explores several cutting-edge platforms and methodologies relevant to these processes.

T cells are key mediators of the adaptive immune response, playing both direct and supporting roles in the destruction of foreign pathogenic threats as well as pathologically transformed host cells. The natural process through which T cells are activated requires coordinated molecular interactions between antigen-presenting cells and T cells. Promising advances in biomaterial design have catalyzed the development of artificial platforms that mimic the natural process of T cell stimulation, both to bolster the performance of cell therapies by activating T cells ex vivo prior to adoptive cell transfer and to directly activate T cells in vivo as off-the-shelf treatments. This review focuses on innovative strategies in T cell-stimulating platform design for applications in cancer therapy. We specifically highlight progress in bead-based artificial antigen-presenting cell engineering, hydrogel-based scaffolds, DNA-based systems, alternative polymeric strategies, and soluble activation approaches. Collectively, these advances are expanding the repertoire of tools for targeted immune activation.