Cell genomics最新文献

Human proteome data across populations have been analyzed extensively to reveal protein quantitative associations with physiological or pathological states, while the single amino acid polymorphism (SAP) has been rarely investigated. In this work, we introduce a pan-SAP workflow that relies on pan-database searching independent of individual genome sequencing. Using ten cohorts comprising 2,004 individuals related to cognition disorder and aging, we quantify the SAP sites in key proteins, such as apolipoprotein E (APOE) in plasma and cerebrospinal fluid at the proteome level. Specifically, the quantification of heterozygous APOE-C112R, including its abundance and ratio, provides insights into the dosage effect and relationship with cognition disorder, which cannot be interpreted at the genomic level. Furthermore, our approach could precisely track age-related changes in APOE-C112R levels. Taken together, this pan-SAP workflow uncovered existing but hidden SAPs in multi-populations, connecting SAP quantification to disease progression and paving the way for broader proteomic investigations in complex diseases.

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

Hector Franco is a group leader at the University of Puerto Rico Comprehensive Cancer Center (UPRCCC), having re-located from the University of North Carolina in 2023. His laboratory focuses on gene regulation, non-coding RNA, and the tumor microenvironment. In this issue of Cell Genomics, his team presents the resource "Defining the regulatory logic of breast cancer using single-cell epigenetic and transcriptome profiling," which highlights non-coding mechanisms of gene regulation in breast cancer.

Understanding the evolution of human traits is a fundamental yet challenging question. In a recent Cell Genomics article, Kun et al.¹ integrate large-scale genomic and phenotypic data, including deep-learning-derived imaging phenotypes, with temporal annotations to estimate the timing of evolutionary changes that led to differences in traits between modern humans and primates or hominin ancestors.

Single-cell RNA sequencing CRISPR (perturb-seq) screens enable high-throughput investigation of the genome, allowing for characterization of thousands of genomic perturbations on gene expression. Ambient gRNAs, which are contaminating gRNAs, are a major source of noise in perturb-seq experiments because they result in an excess of false-positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNAs in perturb-seq screens. We use these datasets to develop CRISPR Library Evaluation and Ambient Noise Suppression for Enhanced single-cell RNA-seq (CLEANSER), a mixture model that filters ambient gRNAs. CLEANSER includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy across multiple screen formats.

Host-parasite relationships drive the evolution of both parties. In microbe-phage dynamics, CRISPR functions as an adaptive defense mechanism, updating immunity via spacer acquisition. Here, we investigated these interactions within the human gut microbiome, uncovering low frequencies of spacer acquisition at an average rate of one spacer every ∼2.9 point mutations using isolates' whole genomes and ∼2.7 years using metagenome time series. We identified a highly prevalent CRISPR array in Bifidobacterium longum spreading via horizontal gene transfer (HGT), with six spacers found in various genomic regions in 15 persons from the United States and Europe. These spacers, targeting two prominent Bifidobacterium phages, comprised 76% of spacer occurrence of all spacers targeting these phages in all B. longum populations. This result suggests that HGT of an entire CRISPR-Cas system introduced three times more spacers than local CRISPR-Cas acquisition in B. longum. Overall, our findings identified key ecological and evolutionary factors in prokaryote adaptive immunity.

Endometriosis is a chronic condition with limited therapeutic options. The molecular aberrations promoting ectopic attachment and interactions with the local microenvironment sustaining lesion growth have been unclear, prohibiting development of targeted therapies. Here, we performed single-cell and spatial transcriptomic profiling of ectopic lesions and eutopic endometrium in endometriosis. We found that ectopic endometrial stromal (EnS) cells retained cyclical gene expression patterns of their eutopic counterparts while exhibiting unique gene expression that contributes to the pathogenesis of endometriosis. We identified two distinct ovarian stromal cells (OSCs) localized at different zones of the lesion, showing differential gene expression profiles associated with fibrosis and inflammation, respectively. We also identified WNT5A upregulation and aberrant activation of non-canonical WNT signaling in endometrial stromal cells that may contribute to the lesion establishment, offering novel targets for therapeutic intervention. These data will enhance our understanding of the molecular mechanisms underlying endometriosis and paves the way for developing non-hormonal treatments.

Single-cell methods to assess DNA methylation have not achieved the same level of cell throughput per experiment compared to other modalities, with large-scale datasets requiring extensive automation, time, and other resources. Here, we describe sciMETv3, a combinatorial indexing-based technique that enables atlas-scale libraries to be produced in a single experiment. To reduce the sequencing burden, we demonstrate the compatibility of sciMETv3 with capture techniques to enrich regulatory regions, as well as the ability to leverage enzymatic conversion, which can yield higher library diversity. We showcase the throughput of sciMETv3 by producing a >140,000 cell library from human middle frontal gyrus split across four multiplexed individuals using both Illumina and Ultima sequencing instrumentation. Finally, we introduce sciMET+ATAC to enable high-throughput exploration of the interplay between chromatin accessibility and DNA methylation within the same cell.

We talk to Xiaoyue Wang and Jinhua Leng, corresponding authors of "Single-cell and spatial transcriptomic profiling revealed niche interactions sustaining growth of endometriotic lesions" in this issue of Cell Genomics, about their research, the key implications of their study, and their advice for other scientists.

Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity. Some populations have greater proportional representation in data relative to their population size and the genomic diversity present in their ancestral haplotypes. As insights from genomics become increasingly integrated into evidence-based medicine, strategic inclusion and effective mechanisms to ensure representation of global genomic diversity in datasets are imperative.