Cell genomics最新文献

Cellular identity emerges from the dynamic coordination of context-aware gene programs that encode biological functions across molecular layers. To decode this complexity, we present SSpMosaic, a computational framework that establishes metaprograms (higher-order, cross-dataset aligned gene program representations) as universal anchors for biological state representation. Leveraging these metaprograms, SSpMosaic enables consistent, accurate integration across batches, modalities, and species. Critically, SSpMosaic accurately annotates cell types within query datasets, enabling discovery and annotation of novel cell states through metaprogram-based transfer learning. The framework achieves resolution-agnostic spatial transcriptomics deconvolution, precisely mapping cell-type distributions from spot-level (Visium) to subcellular scales (CosMx/Visium HD). As a paradigm-shifting application, we integrate single-nucleus transcriptomics, chromatin accessibility, and spatial transcriptomics to resolve multi-stage spatial domain dynamics across tissue slices. Finally, SSpMosaic enables reference-free spatial characterization, identifying conserved spatial ecotypes across tissue slices and annotating cellular niches without requiring matched single-cell data.

Protein domains are fundamental units determining protein functions. This study identified all protein domains and domain combinations from 446 genomes across all major plant lineages. We discovered more domains and domain combinations in land plants than in algae. Many novel "core" protein domains were acquired in the early evolution of streptophytes, substantially enriching the genomic toolkit that enabled plants to shift from unicellular to multicellular organization and to adapt to terrestrial life. After conquering the land, the number of ancestral core domains kept decreasing in land plants; in contrast, an increasing number of non-core domains were acquired, which, together with enhanced activity of domain shuffling, generated various novel domain combinations and expanded protein diversity. We speculate that losing existing genetic elements (core domains) is not always detrimental, as it may have reduced evolutionary constraint upon species, paving the way for biological innovation (speciation) and adaptation to changing environments.

Pre-existing differences between individual cancer cells can predict which cells will become resistant to treatment. DNA barcoding methods that track clones and their cell states during treatment have furthered this understanding, previously focusing on resistance to single treatments. Here, we performed multi-treatment, high-throughput clonal tracking and single-cell RNA sequencing to trace rare clones through resistance development across many treatments in parallel, identifying cell states associated with multi-treatment resistance. We found that clones resistant to one treatment had an increased chance of separately developing resistance to other treatments. We identified high CD44 expression in treatment-naive cells as a predictor of future multi-treatment resistance. Additionally, we found that differences in pre-treatment gene expression states can lead cells within the same treatment condition to follow divergent paths toward their ultimate resistance fate. This work provides a framework for extracting targetable gene expression states from complex resistance dynamics to eliminate multi-treatment resistance.

Cell-lineage tracing now enables direct study of tissue migration in metastatic cancer, but current reconstruction algorithms are limited by a reliance on strong parsimony assumptions and pre-estimated cell-lineage phylogenies. Here, we introduce a probabilistic modeling and inference framework, called Bayesian Evolutionary Analysis of Metastasis (BEAM), which provides richer information about complex metastatic histories. Based on the flexible BEAST 2 platform for Bayesian phylogenetics, BEAM infers a full posterior distribution over cell-lineage phylogenies and tissue-migration graphs, complete with timing information. We show using simulated data that BEAM reliably outperforms current methods for inference of tissue-migration graphs, especially for more complex histories. We then apply BEAM to public datasets for lung and prostate cancer, finding support for distinct modes of migration across clones and reseeding of primary tumors. Overall, BEAM serves as a powerful framework for revealing the modes, timing, and directionality of tissue migration in metastatic cancer.

NOTCH2NL (NOTCH2-N-terminus-like) genes arose from ape-specific chromosome 1 segmental duplications implicated in human brain cortical expansion, including an incomplete NOTCH2 gene. Genetic characterization of these loci and their regulation is complicated because they are embedded in large, nearly identical duplications that predispose to recurrent microdeletion syndromes. Using near-complete long-read assemblies generated from 70 human and 12 ape haploid genomes, we show independent recurrent duplication among apes with protein-coding copies emerging in humans 2.2-3.7 million years ago. We distinguish NOTCH2NL paralogs present in every human haplotype (NOTCH2NLA) from copy-number-variable ones. We also characterize large-scale structural variation, including gene conversion, for 28% of haplotypes, leading to a previously undescribed paralog, NOTCH2tv. Finally, we apply Fiber-seq and long-read transcript sequencing to human dorsal forebrain organoids to characterize the regulatory landscape and find that the most fixed paralogs, NOTCH2 and NOTCH2NLA, harbor the greatest number of paralog-specific elements potentially driving their regulation.

The continued development of high-dimensional CRISPR screen readouts, such as single-cell RNA sequencing and high-content imaging, necessitates compact libraries to enable functional interrogation at genome scale. Improved genome annotations cause library deprecation over time, further motivating an updated genome-wide design effort. Additionally, while on-target efficacy and off-target avoidance are often optimized in isolation, we lack a robust framework for simultaneously weighing and balancing these competing priorities. Here, we present a selection strategy that identifies guides with sufficient off-target activity to justify omission from the library, thus avoiding the unnecessary exclusion of active guides, allowing the inclusion of those with maximal on-target activity. We create, validate, and make available to the community the Jacquere library for knockout screens of the human genome, as well as its mouse counterpart, Julianna, to facilitate gene function discovery at scale.

It remains unknown whether early embryonic cells harbor a blueprint for future enhancers that regulate the expression of lineage-specific genes in adult tissues. Here, we demonstrate that embryonic stem cells (ESCs) have transcriptionally competent chromatin regions (CCRs) prepared to induce the expression of lineage genes prior to differentiation. CCRs represent activatable pre-enhancers within the topological chromatin domains of lineage genes, marked by chromatin signatures distinguishable from primed/poised enhancers, enabling their genome-wide identification. The pioneer transcription factor (TF) FOXA2 preferentially binds CCRs during early lineage specification, promoting their conversion into active enhancers. CCRs can be harnessed to boost the expression of master TFs and promote the direct reprogramming of ESCs into differentiated cells, showcasing their potential for practical applications. Our findings identify a mechanism by which ESCs rapidly establish enhancer activity during early lineage differentiation and expand our understanding of the epigenetic features supporting transcriptional regulation and cellular plasticity.

In this meet-the-author Q&A, Scientific Editor Sara Rohban and Editor-in-Chief Laura Zahn speak with Junyue Cao about his Cell Genomics paper. He discusses his ambitions to study aging and how his newly developed method, EnrichSci, was used to look at changes over time in oligodendrocytes in the brain.

Red blood cells (RBCs) transport oxygen but accumulate oxidative damage over time, reducing function in vivo and during storage, critical for transfusions. To explore the genetics of RBC resilience, we profiled proteins, metabolites, and lipids from fresh and stored RBCs from 350 genetically diverse mice. Our analysis identified over 6,000 quantitative trait loci (QTLs). Compared to other tissues, the prevalence of trans genetic effects over cis ones reflects the absence of de novo protein synthesis in anucleated RBCs. QTL hotspots at Hbb, Hba, Mon1a, and (storage-specific) Steap3 linked ferroptosis to hemolysis. Proteasome QTLs clustered at multiple loci, underscoring the importance of degrading oxidized proteins. Post-translational modification (PTM) QTLs mapped predominantly to hemoglobins, including cysteine residues. The loss of reactive C93 in humanized mice (hemoglobulin beta [HBB] C93A) disrupted redox balance, glutathione pools, glutathionylation, and redox PTMs. These findings highlight genetic regulation of RBC oxidation, with implications for transfusion biology and oxidative-stress-dependent hemolytic disorders.

Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.