1 Cell  Proteomic Landscape of Synaptic Diversity

Proteomic Landscape of Synaptic Diversity

Neuronal synapses are composed of different participating proteins, and these protein combinations determine the specificity, function, and plasticity of synapses. However, the diversity of the synaptic proteome remains largely unexplored. The research team of Erin M. Schuman at the Max Planck Institute for Brain Research in Germany prepared synaptosomes with fluorescently labeled presynaptic terminals from 7 different transgenic mouse lines, combined with fluorescence-activated synaptosome sorting to microdissect 5 different brain regions, and discovered a total of more than 1,800 synaptic proteins, identifying 18 synapse types formed by these proteins. Through correlation network analysis, they revealed excitatory and inhibitory protein communities. The researchers created a network interaction tool for querying the abundance and related functions of a certain protein in a specific synapse type, and described the proteomic characteristics of three subtypes of striatal dopaminergic synapses and cortical GABAergic synapses, highlighting the functional differences among these synaptic proteins.

Immunopeptidomics Study Discovers a Novel CAR-T Therapy

Chimeric antigen receptor (CAR) T-cell therapy has shown outstanding efficacy in the treatment of leukemia and some solid tumors. However, most oncogenic factors are intracellular proteins, and their immunotherapeutic targets can only rely on mutated peptides (neoantigens) presented by individual human leukocyte antigens. Yet most cancers have a small mutational load, insufficient to generate specific responses based on neoantigen therapy. Neuroblastoma arises mainly from epigenetic transcriptional dysregulation and has few mutations. Researchers including John M. Maris at the Perelman School of Medicine, University of Pennsylvania, and Mark Yarmarkovich at NYU Grossman School of Medicine, screened and identified specific antigens derived from non-mutated tumor proteins, and based on these constructed a peptide-centric, scFv-based novel engineered CAR T cell. Through in vitro and in vivo experiments, they demonstrated its highly efficient elimination of neuroblastoma cells, providing a novel approach for immunotherapy targeting intracellular oncoproteins.

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Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with high resistance to treatment. Inflammatory and immunomodulatory signals coexist in the pancreatic tumor microenvironment, leading to dysregulated repair and cytotoxic responses. Tumor-associated macrophages (TAMs) are relevant targets in immuno-oncology and play a key role in PDAC. The research team of Nicoletta Caronni and Renato Ostuni at the San Raffaele Scientific Institute in Italy used single-cell and spatial genomics to reveal the important role of macrophages in pancreatic cancer. This study found that in early PDAC, prostaglandin E2 (PGE2) and tumor necrosis factor (TNF) synergistically induce the production of the inflammatory macrophage subset IL-1β+ TAMs. The authors further studied the correlation between the spatial omics characteristics of IL-1β+ TAMs and the inflammatory reprogramming and pathogenicity of PDAC cell subsets, ultimately finding that blocking PGE2 or IL-1β activity can trigger TAM reprogramming and antagonize both intrinsic and extrinsic inflammation of tumor cells, thereby controlling PDAC in vivo. Therefore, targeting PGE2-IL-1β may provide a new preventive or therapeutic strategy for reprogramming the immune dynamics of pancreatic cancer.

The maturation of single-cell transcriptomics technology has facilitated the generation of comprehensive cell atlases from whole embryos. However, most of these data were collected from wild-type embryos, without evaluating the potential variation during development. The research team of David Kimelman and Cole Trapnell at the University of Washington used single-cell RNA sequencing to assess developing zebrafish models, cumulatively obtaining single-cell transcriptomic data from 1,812 individually resolved developing zebrafish embryos, including 3.2 million cells, 19 time points, and 23 genetic perturbations. The high reproducibility of this study (8 or more embryos per condition) enabled the researchers to estimate changes in cell type abundance across the whole organism and to detect perturbation-dependent deviations relative to wild-type embryos. The method is sensitive to rare cell types, resolving the developmental trajectory and genetic dependencies of brain ganglion neurons that account for less than 1% of the embryo. In addition, time-series analysis of individual mutants identified a group of short-axis-independent cells with a transcriptome strikingly similar to that of notochord sheath cells, suggesting a new hypothesis about the early origin of the skull. The authors anticipate that high-resolution single-cell data from standardized collection of large numbers of individual embryos will help to obtain a genetic dependency cell atlas of zebrafish, while also addressing long-standing challenges in developmental genetics, including cellular and transcriptional plasticity underlying individual phenotypic diversity.

Cellular function is mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled with mass spectrometry is an ideal tool for this type of research, but it is difficult to identify low-copy-number complexes, membrane complexes, and complexes disrupted by protein tagging. The team of Mattias Mann at the Max Planck Institute of Biochemistry in Germany developed a highly sensitive, high-throughput method using affinity purification mass spectrometry (AP-MS) combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. By analyzing the interacting proteins of 4,000+ yeast proteins, they resolved a highly structured network composed of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps. The high connectivity of the resulting network is reflected in an average shortest path of 4.2 between yeast proteins, which is very similar to the 4.7 path length of human social relationships on Facebook.

Sensitive and reliable protein biomarkers can be used to predict the disease trajectory and personalized treatment strategies of multiple sclerosis (MS). Researchers including Mika Gustafsson at Linköping University in Sweden used the highly sensitive proximity extension assay (PEA) combined with next-generation sequencing to quantitatively detect 1,463 proteins in the cerebrospinal fluid (CSF) and plasma of 143 patients with early MS and 43 healthy controls. Through long-term follow-up, the researchers found that a lower level of neurofilament light chain (NfL) in CSF has an advantage in predicting no disease activity two years after sampling (AUC = 0.77), and further determined a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B, and NfL) that can consistently predict short- and long-term disease progression, predicting the severity of disability worsening according to the standardized age-related MS severity score (AUC = 0.90). The identification of these proteins may help to elucidate the disease process and aid in the formulation of treatment strategies for MS patients.