Jingyi Li

Nov 21, 2024 | nature.com | Wilson X. Mai |Kai Song |Nicholas Bayley |Jiyoon Kim |Henan Zhu |Pauline Young | +14 more

Genomic profiling often fails to predict therapeutic outcomes in cancer. This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses. It is unclear whether integrating genomic and functional features of solid tumours can provide unique insight into therapeutic vulnerabilities. We perform combined molecular and functional characterization, via BH3 profiling of the intrinsic apoptotic machinery, in glioma patient samples and derivative models. We identify that standard-of-care therapy rapidly rewires apoptotic signaling in a genotype-specific manner, revealing targetable apoptotic vulnerabilities in gliomas containing specific molecular features (e.g., TP53 WT). However, integration of BH3 profiling reveals high mitochondrial priming is also required to induce glioma apoptosis. Accordingly, a machine-learning approach identifies a composite molecular and functional signature that best predicts responses of diverse intracranial glioma models to standard-of-care therapies combined with ABBV-155, a clinical drug targeting intrinsic apoptosis. This work demonstrates how complementary functional and molecular data can robustly predict therapy-induced cell death. Genomic profiling of tumours can help tailer treatments to the patient, however, it often fails to accurately predict therapeutic outcomes. Here, the authors combine molecular and functional characterisation via BH3 profiling to identify therapeutically targetable vulnerabilities in glioma.

Nov 19, 2024 | biorxiv.org | Pan Liu |Jingyi Li

AbstractIn single-cell data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries.

Nov 1, 2024 | biorxiv.org | Pan Liu |Jingyi Li

AbstractIn single-cell data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing down- stream analysis and leading to spurious discoveries.

Jul 17, 2024 | nature.com | Jingyi Li

AbstractPrevious studies have reported low self-esteem contributes to depressive symptoms among adolescents, but the underlying mechanism remains unclear. The present study aimed to examine the mediating roles of hope and anxiety in the relationship between self-esteem and depressive symptoms. 431 adolescents between 13 and 18 years volunteered to complete a battery of questionnaires that included measures on the variables mentioned above.

Jun 16, 2024 | arxiv.org | Jingyi Li |Lin Chen |Xu Chen