January Selected literature from CNS journals
1 Cell
Proteogenomic profiling of small cell lung cancer reveals its molecular characteristics and subtype-specific therapeutic strategies
2 Analytical Chemistry
RUPE-phospho: a rapid ultrasound-assisted, peptide identification-enhanced phosphoproteomic workflow for minute samples
3 Science
Deterministic reprogramming of neutrophils within tumors
4 Nature Communication
Plasma proteomics predicts an individual's future health risks
5 Nature Biotechnology
Mosaic integration and knowledge transfer for single-cell multimodal data with MIDAS
6 Science
"Contrast-enhancing" cell-free DNA to improve liquid biopsy sensitivity
7 Cell
DNA-guided transcription factor cooperativity shapes facial and limb mesenchyme
Proteogenomic profiling of small cell lung cancer reveals its molecular characteristics and subtype-specific therapeutic strategies
Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies
Inactivating mutations in TP53 and RB1 are widespread in small cell lung cancer (SCLC), making it difficult to obtain effective targets or molecular subtypes from gene mutation information alone. A team led by Professor Zhang Peng of Tongji University School of Medicine and Shanghai Pulmonary Hospital, working with the team of Professor Zhou Hu at the Shanghai Institute of Materia Medica of the Chinese Academy of Sciences and the teams of Professor Ji Hongbin and Professor Gao Daming at the CAS Center for Excellence in Molecular Cell Science, performed comprehensive proteogenomic characterization of tumor tissues and matched adjacent tissues from 112 patients with primary SCLC who underwent surgical resection. Integrated multi-omics analysis revealed the cancer biology downstream of genetic aberrations and highlighted the oncogenic roles of FAT1 mutations, RB1 loss and chromosome 5q loss. The study identified two prognostic biomarkers, HMGB3 and CASP10. Overexpression of HMGB3 promotes SCLC cell migration through transcriptional regulation of cell junction-related genes. Characterization of the immune microenvironment revealed an association between ZFHX3 mutations and high immune infiltration, and highlighted the potential immunosuppressive effect of increasing DNA damage response activity through inhibition of the cGAS-STING pathway. Using multi-omics data and a non-negative matrix factorization algorithm, the researchers classified SCLC into four subtypes (nmf1–4), systematically characterized the distinct molecular features of each subtype and proposed potential therapeutic strategies. This study provides a valuable resource and important insights for better understanding SCLC biology and improving clinical practice.
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RUPE-phospho: a rapid ultrasound-assisted, peptide identification-enhanced phosphoproteomic workflow for minute samples
RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples
Phosphorylation is a major post-translational regulatory mechanism in living organisms, and many human diseases are linked to abnormal protein phosphorylation. In clinical cohort studies, phosphoproteomic analysis therefore makes it possible to understand or explain disease onset and progression from the perspective of phosphorylation, serving as an important complement to proteomics-driven precision medicine (PDPM). Current phosphoproteomic sample preparation, however, is time-consuming, involves cumbersome steps and requires a large amount of starting protein. With clinical cohorts growing ever larger and pathological samples ever smaller, conventional phosphoproteomic workflows struggle to meet today's research needs. A team led by Academician He Fuchu at the National Center for Protein Sciences (Beijing) developed a non-contact ultrasound-assisted, high-throughput, micro-scale phosphoproteomic strategy (RUPE-phospho). The strategy uses a temperature-controlled non-contact ultrasonic device for rapid protein digestion and optimizes the subsequent enrichment workflow for minute samples; the whole workflow completes the preparation of 24 phosphoproteomic samples in just 3 hours. From a 5 μg cell lysate sample, 5,325 phosphorylation sites, 4,549 phosphopeptides and 1,888 phosphoproteins were identified. The method also performed well on mouse frozen tissue sections (OCT tissue): 1–2 mg of OCT-embedded mouse brain tissue yielded 9,219 phosphorylation sites.
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Deterministic reprogramming of neutrophils within tumors
Deterministic reprogramming of neutrophils within tumors
Neutrophils are the most abundant white blood cells in bone marrow and blood. In the immune response to external infection and injury, they are rapidly recruited into damaged tissue and perform key immunoregulatory functions. Because of their short lifespan and the difficulty of culturing them in vitro, they have long been a blind spot in immunology research; the differentiation and developmental paths of intratumoral neutrophils, and the way they are uniformly remodeled into pro-tumor functions, remain poorly understood. To explore the heterogeneity of intratumoral neutrophils, the research team of Ng Lai Guan at the Shanghai Institute of Immunology and Innovation, Renji Hospital affiliated to Shanghai Jiao Tong University School of Medicine, used single-cell RNA sequencing (scRNA-seq) to identify neutrophils in the tumor microenvironment of an orthotopic pancreatic ductal adenocarcinoma (PDAC) mouse model. They revealed that neutrophils, whether mature or not, can remodel in response to environmental cues and can follow a common differentiation path to converge into a terminal T3 pro-tumor phenotype. In addition, reprogrammed dcTRAIL-R1+ neutrophils localize mainly to the glycolytic and hypoxic regions of the tumor core, where they perform pro-angiogenic functions and promote tumor growth. Similar neutrophil trajectories were found across multiple tumor types and in humans, suggesting that targeting this process may offer a way to strengthen cancer immunotherapy.
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Plasma proteomics predicts an individual's future health risks
Plasma proteomic profiles predict individual future health risk
Risk stratification is essential for identifying high-risk individuals and preventing disease, but the time and cost involved in gathering information severely limit the practical clinical use of risk scores for each individual disease; an assay capable of predicting the risk of multiple diseases at once is therefore critically needed. A team led by researcher Cheng Wei and Professor Feng Jianfeng at the Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, together with the team of Professor Yu Jintai at Huashan Hospital, Fudan University, trained neural networks on data from 1,461 Olink plasma protein assays in 52,006 UK Biobank (UKB) participants to develop a disease- and mortality-specific proteomic risk score (ProRS) method. Significant risk stratification was achieved for 45 common diseases. For almost all endpoints, ProRS performed as well as or better than established clinical indicators. Combining clinical predictors with ProRS improved prediction for most endpoints, but this combination showed only limited improvement over ProRS alone. In addition, some proteins (such as GDF15) were found to have important discriminative value across a range of diseases. The study shows that proteomic profiles can serve as a substitute for complex laboratory tests or clinical measures, refining comprehensive risk assessment across multiple diseases and mortality simultaneously. Naturally, more independent external validation will still be needed to confirm these findings before clinical application.
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Mosaic integration and knowledge transfer for single-cell multimodal data with MIDAS
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS
Single-cell sequencing is a breakthrough technology that has emerged in the life sciences in recent years: it detects multiple kinds of genetic material and functional molecules within a single cell, revealing cellular heterogeneity at different molecular layers and the relationships between omics layers. As sequencing technologies advance and data volumes grow, however, integrating "mosaic" single-cell data — different omics combinations, different sequencing technologies and different samples — has become a major challenge. Teams led by Ying Xiaomin and Bo Xiaochen at the Academy of Military Medical Sciences independently developed MIDAS, a new algorithm based on generative artificial intelligence. MIDAS can efficiently and flexibly transfer knowledge from a reference dataset to a query dataset, making it easier to work with new single-cell multi-omics data. In addition, the latent variables produced by MIDAS dimensionality reduction allow pseudotime analysis of mosaic data with missing modalities. When transferring knowledge across different tissues, MIDAS also performs strongly in aligning heterogeneous datasets, identifying known cell types and discovering unknown ones.
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"Contrast-enhancing" cell-free DNA to improve liquid biopsy sensitivity
Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies
Liquid biopsy can detect and monitor diseases such as cancer at an early stage, but its sensitivity is still limited by the low concentration of analytes such as cell-free DNA (cfDNA) in blood. Improvements in sensitivity have so far relied mainly on advances in in vitro sequencing technology. Researchers including Viktor Adalsteinsson at the Massachusetts Institute of Technology and the Broad Institute sought to transiently boost circulating tumor DNA (ctDNA) levels by slowing its clearance in vivo. The main approach was to suppress its degradation: liposomes were used to compete with liver macrophages for the uptake of cfDNA, and antibodies (which bind DNA but lack Fcγ receptor binding capacity) were used to inhibit the degradation of cfDNA by DNases. This approach greatly increased ctDNA recovery and improved sensitivity for detecting small tumors.
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DNA-guided transcription factor cooperativity
shapes facial and limb mesenchyme
DNA-guided transcription factor cooperativity shapes face and limb mesenchyme
Transcription factors (TFs) can define distinct cell identities even when their DNA-binding specificities are almost identical. One mechanism for achieving regulatory specificity is DNA-guided TF cooperativity. Although in vitro studies suggest that such cooperativity may be widespread, examples in the cellular context remain scarce. The team of Joanna Wysocka at Stanford University revealed how a special DNA motif called "Coordinator" precisely regulates the development of embryonic facial and limb bud mesenchyme by promoting cooperation among multiple transcription factors. Among these, the cooperative interaction between TWIST1 and a group of HD factors is essential for maintaining open chromatin at regulatory regions and for determining cell type and positional identity, further influencing facial morphology and evolution. Although previous in vitro experiments suggested that DNA-guided transcription factor cooperativity is widespread, this study provides concrete and rare examples of such cooperativity in a cellular setting.
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