Cell Reports Methods最新文献

Generating a large number of progenitors that can repopulate the immune system of a recipient is one of the key steps toward efficient cancer immunotherapy. Here, we describe the engineering of T cell progenitors capable of direct and long-term reconstitution of the thymus. In the thymus, human pluripotent stem cell (hPSC)-derived progenitor T cells (pro-T cells) developed into single-positive human T cells that entered circulation and settled in the spleen. Single-cell transcriptome analysis of differentiating hPSCs attested to the emergence of cells that displayed the transcription signature of the early T cell progenitors. Comparative transcription profiling revealed the similarity of the hPSC-pro-T cells with the early T cell precursors of the human thymus. The in vitro generation of T cell progenitors provides a powerful model for studying the molecular mechanisms of human T cell development and improves the perspectives for T cell regenerative medicine, including chimeric antigen receptor T (CAR-T) cell therapies.

CRISPR-mediated gene editing using engineered virus-like particles (eVLPs) can achieve high efficiency, but performance varies with reduced effectiveness often seen in primary cells or when generating polyclonal models at scale. We developed a faster, accurate and 4-fold more efficient CRISPR-Cas9 (FAME-CRISPR) method using pan-histone deacetylase inhibitors with eVLP transduction compared to previous reports using other histone deacetylase inhibitors. Combined optimization of pan-HDACi treatment with eVLP enhanced double-strand break (DSB)-mediated CRISPR and base editing gave significantly edited populations within 2- to 3-cell mean population doublings, reducing the need for post-editing selection in immortalized cancer cells and in primary diploid fibroblasts that have limited replicative lifespans.

Understanding human cell metabolism through genome-scale flux profiling is of interest to diverse research areas of human health and disease. Metabolic modeling using genome-scale metabolic models (GEMs) has the potential to achieve this, but has been limited by a lack of appropriate input data as model constraints. Here, we compare the commonly used consumption and release (CORE) method to a regression-based method (regression during exponential growth phase; REGP). We found that the CORE method is not reliable despite being prevalent in human studies, whereas the exchange fluxes determined by REGP provide constraints that substantially improve GEM simulations for human cell lines. Our results show that the GEM-simulated feasible flux space is constrained to a biologically plausible region, allowing an exploration of the basic organizing principles of the feasible flux space. These improvements help to fulfill the promise of GEMs as a valuable tool in the study of human metabolism and future development of translational applications.

AGO-APP through the expression of the T6B peptide permits the isolation of Ago-bound microRNAs (miRNAs). Here, we present the generation and characterization of two transgenic mouse lines that enable AGO-APP to be performed in vivo. First, we generated mice for CRE-dependent T6B expression throughout the cell. Using this line, we performed AGO affinity purification (AGO-APP) in olfactory bulb (OB) inhibitory interneurons and cerebral cortex excitatory neurons. Bioinformatic analysis validated the high reproducibility of the approach. It also demonstrated that, despite global miRNome conservation between the two cell types, a set of miRNAs, including the miR-200 family and the miR-183/96/182 cluster, is massively enriched in OB interneurons, which aligns with previous observations. In the second mouse line, T6B is fused to the postsynaptic protein PSD95. Isolation of T6B-PSD95 fractions from OB and cortical neurons identified specific sets of postsynapse-enriched miRNAs. Gene ontology analyses confirmed that these miRNAs preferentially target mRNAs related to synaptic functions.

Real-time monitoring of microbial communities offers valuable insights into microbial dynamics across diverse environments. However, many existing metagenome analysis tools require advanced computational expertise and are not designed for monitoring. We present MMonitor, an open-source software platform for real-time analysis and visualization of metagenomic Oxford Nanopore Technologies (ONT) sequencing data. MMonitor includes two components: a desktop application for running bioinformatics pipelines through a graphical user interface (GUI) or command-line interface (CLI) and a web-based dashboard for interactive result inspection. The dashboard provides taxonomic composition over time, quality scores, diversity indices, and taxonomy-metadata correlations. Integrated pipelines enable automated de novo assembly and reconstruction of metagenome-assembled genomes (MAGs). To validate MMonitor, we tracked human gut microbial populations in three bioreactors using 16S rRNA gene sequencing and applied it to whole-genome sequencing (WGS) data to generate high-quality annotated MAGs. We compare MMonitor with other real-time metagenomic tools, outlining their strengths and limitations.

Nuclear magnetic resonance (NMR) spectroscopy is often used for the analysis of metabolites in proteinaceous biological specimens. However, the binding of metabolites to proteins impedes accurate quantitation of total metabolite concentrations by NMR, unless protein binding is disrupted by organic solvent precipitation, which increases variance and may result in the loss of volatile metabolites during post-extraction drying. Here, we present an approach for the inference of total metabolite concentrations from Carr-Purcell-Meiboom-Gill NMR spectra via computation of metabolite and sample-specific factors derived from the individual broadening of spectral peaks due to protein-metabolite binding. The method was validated on both synthetic proteinaceous samples and plasma and urine specimens including a certified reference plasma. Furthermore, results were compared with those obtained for methanol extracts of plasma specimens. In summary, our approach obviates the need for protein precipitation, is easy to use, and allows precise and reliable determination of total metabolite concentrations.

Expanding metabolomic profiling to the single-cell level can reveal metabolic heterogeneity and clinically relevant subpopulations, yet existing methods lack sensitivity and scale. To address this gap, in a recent issue of Cell, Delafiori and colleagues introduce HT SpaceM, a high-throughput MALDI workflow enabling sensitive, reproducible, and scalable single-cell metabolomics.

Studies using genetic tagging and optogenetics demonstrated that reactivation of memory engrams, neuronal ensembles encoding specific learned information, can trigger memory recall and that synaptic potentiation among engram neurons is critical for memory persistence. However, the complexity of intact brain networks has limited mechanistic access to the processes underlying engram formation. Here, we introduce a hybrid in vitro platform that recapitulates, in a simplified and controllable setting, the core principles used in vivo to activate engrams. By combining digital light processing (DLP) with optogenetics, we imposed Hebbian co-activation of two targeted neurons, inducing the emergence of a functional cell assembly module. This artificial co-firing produced synaptic strengthening and spatial clustering of potentiated spines along the dendrites connecting the co-activated neurons, hallmarks of engram connectivity. Our system provides a reductionist yet biologically relevant framework to dissect, with high spatial and temporal resolution, the cellular and molecular determinants of cell assembly formation.

Brown algae represent the third most complex lineage to have independently evolved multicellularity, distinct from plants and animals. Yet, functional studies of their development and evolution have been limited by the absence of efficient genome editing tools. Here, we present a robust, high-efficiency, and transgene-free CRISPR-based genome editing platform applicable across four ecologically and biotechnologically important brown algal species. Using Ectocarpus as a model, we optimized a polyethylene glycol (PEG)-mediated ribonucleoprotein (RNP) delivery system that achieves reproducible editing across multiple loci without cloning or specialized equipment. As proof of concept, we recreated the hallmark imm mutant phenotype by precisely editing the IMMEDIATE UPRIGHT (IMM) locus. APT/2-fluoroadenine (2-FA) selection further enhanced specificity with minimal false positives. The method was easily transferable to other species, including kelps. This platform now enables functional genomics in brown algae, providing powerful tools for investigating development, life cycle regulation, and the independent evolution of complex multicellularity.

Accurate measurement of energy expenditure is critical for metabolic research and public health. Doubly labeled water (DLW) is the gold standard for assessing free-living energy expenditure, yet inconsistencies in equations impede comparability across studies. This analysis evaluates a newly proposed standardized equation of energy expenditure from the DLW method against commonly employed historical equations. Using validation data from whole-room indirect calorimetry, we demonstrate that the new equation offers improved accuracy. Further, analysis of a large historical dataset and mathematical modeling revealed a systematic bias of ∼1.6%, indicative of an underestimation of energy expenditure estimates with the new equation compared to previous equations. Application of a newly developed correction factor mitigated this bias, allowing for closer alignment between equations. These findings support adoption of the new standard equation and offer a corrective approach for harmonizing data, thereby facilitating methodological consistency in DLW studies and allowing for the preservation of the utility of historical datasets.