Biomedical subjects

Tingting Shi

Publications and source records attributed to Tingting Shi.

3 recordsLinked to original sources

PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗

PubMed2026

Library-based, multiplexed strategy for mapping protein interaction networks via crosslinking.

BACKGROUND: Protein-protein interactions are fundamental to cellular function, yet resolving their interaction interfaces and dynamic behaviors in native biological contexts remains challenging, particularly for weak or transient interactions. Crosslinking strategies based on noncanonical amino acids offer an effective means to capture such interactions; however, traditional single-site incorporation provides limited coverage and may overlook critical interaction hotspots. RESULTS: By employing a mutagenesis library, multiple interaction partners and cross-linking sites of a target protein can be simultaneously screened in a single experiment, without prior knowledge of its precise structural or functional features, enabling effective and unbiased analysis of its interaction network. In this study, we constructed an amber codon-scanning mutagenesis library of PSMD10, facilitating independent incorporation of the photocrosslinking ncAA p-azido-phenylalanine at multiple distinct residues. This approach allowed us to systematically interrogate and precisely map potential interaction regions across the protein surface. Coupled with crosslinking mass spectrometry, we identified multiple residues involved in intermolecular interactions, as well as previously unreported interaction partners, including T2FA, TBA1C, and ATRIP. CONCLUSIONS: These findings expand our understanding of PSMD10-associated proteasome interactome, demonstrate a multiplexed strategy for in situ mapping of protein interaction interfaces with broad coverage, and offer a valuable platform for developing therapeutics that target protein-protein interactions.

Protein Interaction Mapping↗

PubMed2025

Evolutionary Genomics Unravels the Responses and Adaptation to Climate Change in a Key Alpine Forest Tree Species.

Despite widespread biodiversity loss, our understanding of how species and populations will respond to accelerated climate change remains limited. In this study, we integrate population genomics, experimental evolution, and environmental modeling to elucidate the evolutionary responses to climate change in Populus lasiocarpa, a key alpine forest tree species primarily distributed in the mountainous regions of a global biodiversity hotspot. Over historical timescales, our findings demonstrate that demographic dynamics, divergent selection, and long-term balancing selection have shaped and maintained genetic variation within and between populations. In examining genomic signatures of contemporary climate adaptation, we found that haplotype blocks, potentially caused by inversion polymorphisms that suppress recombination, are linked to enriched combinations of locally adaptive environmental variations. We further assessed the relative contributions of environmentally induced plastic responses, constitutive expression divergence between genetic clusters, and their interactions in driving gene expression variation and divergence. Notably, we observed a strong correlation between sequence divergence and constitutive differential expression among genetic clusters. Finally, by incorporating genetic adaptation, migration, and genetic load into our predictions of population-level climate change risks, we identified western populations-primarily distributed in the Hengduan Mountains, a region known for its environmental heterogeneity and significant biodiversity-as the most vulnerable to climate change. These populations should be prioritized for conservation and management. Overall, our study advances the understanding of the relative roles of long-term natural selection, local environmental adaptation, and immediate plastic expression changes in shaping the responses of natural populations of keystone species to climate change.

Climate Change↗