Cell Reports Methods最新文献

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.

Understanding transcription dynamics in rapidly changing systems requires separating information about newly synthesized transcripts from bulk transcript data. Here, we developed newly synthesized transcriptome on 10× expression sequencing (NOTE-seq), a method for simultaneous profiling of regular and newly synthesized transcriptomes in single cells with high cellular throughput. NOTE-seq integrates 4-thiouridine labeling of newly synthesized RNA, thiol-alkylation-based chemical conversion, and a streamlined 10× Genomics workflow, making it accessible and convenient for biologists without extensive single-cell expertise. Using NOTE-seq, we investigated the temporal dynamics of gene expression during early-stage T cell activation, identified transcription factors and regulons in Jurkat and naive T cells, and revealed that FLI1 downregulation is a key event during T cell stimulation. Notably, topoisomerase inhibition led to the depletion of both topoisomerases and FLI1 in T cells through a proteasome-dependent mechanism. This degradation was driven by topoisomerase cleavage complexes rather than topoisomerase catalytic inhibition, highlighting potential complications topoisomerase-targeting cancer chemotherapies could pose to the immune system.

Next-generation sequencing requires accuracy, reproducibility, and standardized reference materials. The Sequencing Quality Control (SEQC-2) multicenter studies on paired breast cancer and B cell lines generated extensive genomic datasets, but integrated epigenomic and proteomic references remain limited. Here, we performed Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Methyl-seq, RNA sequencing (RNA-seq), and proteomic profiling to establish comprehensive multi-omics reference materials. We identified >7,700 protein groups, with 95% of genes encoding a single peptide isoform. Protein expression from CpG island (CGI)-overlapping transcripts was higher than non-CGI transcripts in both cell lines. Certain SNVs were incorporated into mutated peptides. Chromatin accessibility was regulated by CG density: CG-rich regions showed lower methylation, greater accessibility, and higher gene/protein expression, whereas CG-poor regions exhibited higher methylation, reduced accessibility, and cell line-specific expression patterns. These datasets provide well-defined genomic, epigenomic, transcriptomic, and proteomic characterizations that can serve as benchmarks for validating omics assays and bioinformatics methods, offering a valuable community resource.

The novel object recognition (NOR) test is widely used to assess memory in rodents, offering strong ethological validity, cross-species relevance, and specificity for hippocampal-parahippocampal function. However, standard implementations are often confounded by uncontrolled factors. Here, we present a fully automated, homecage-based NOR test for evaluating long-term object memory in mice. Our empirically informed computational model demonstrates the robustness of this approach despite uncertainties in defining exploratory behavior. Mice reliably preferred novel over familiar objects after both 24-h and 7-day delays, with recognition emerging already at a distance. Results were replicated across two facilities. Notably, recognition after 24 h depended on prior interactions with the replaced object, but not after 7 days. We also show that external factors can bias exploration, which can be mitigated using relative discrimination measures. This automated paradigm enhances standardization, reproducibility, and our understanding of the factors influencing object exploratory behaviors and object memory.

Polyploidy (whole-genome duplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.

Here, we introduce CRISPR and transcriptomics-assay for transposase-accessible chromatin (CAT-ATAC), a technique that adds CRISPR guide RNA (gRNA) capture to the existing 10× Genomics Multiome assay, generating linked transcriptome, chromatin accessibility, and perturbation identity data from the same individual cells. We demonstrate up to 77% capture rate for both arrayed and pooled delivery of lentiviral gRNAs in induced pluripotent stem cells (iPSCs) and cancer cell lines. This capability allows us to construct gene regulatory networks (GRNs) in cells under drug and genetic perturbations. By applying CAT-ATAC, we identified a GRN associated with dasatinib resistance, indirectly activated by the HIC2 gene. Using loss-of-function experiments, we further validated that ZFPM2, a component of the predicted GRN, also contributes to dasatinib resistance. CAT-ATAC can thus be used to generate high-content multidimensional genotype-phenotype maps to reveal gene and cellular interactions and functions.

Spatial mapping of multi-slice multi-omics data enables the identification of shared and slice-specific cellular components across spatiotemporal axes. However, conventional graph neural networks assume uniform contributions from neighboring cells, neglecting directional and angular influences that shape central cell states and limiting their ability to dissect complex spatial structures. Here, we present stLVG, a vector-guided lightweight graph model for spatial mapping, label transfer, and niche identification across multi-slice multi-omics datasets. Specifically, stLVG (1) learns two distinct shared feature spaces across slices by aggregating neighbor information through adversarial learning with distance- and direction-informed weights and (2) integrates these features via a multi-view contrastive learning framework. Compared to existing methods, stLVG achieves superior performance across technologies, modalities, and resolutions; it accurately delineates tumor edge regions in breast cancer samples. Notably, it uses pre-computed weights and can be efficiently executed on a standard laptop within minutes, ensuring scalability to large-scale spatial omics analyses.