Sidi Chen

This paper introduces cTACs, synthetic molecules that degrade PD-L1 across immune and tumor cells, enhancing T cell activation and tumor suppression in vivo.

This study introduces spatial-GPT, a platform for spatial glycan, protein, and transcriptome profiling in human liver tissues, revealing glyco-codes that define disease states and progression.

eSIG-Net predicts how single mutations affect protein interactions using a sequence-based interaction language model, outperforming existing methods.

This paper introduces CAR-T-drug conjugates (CAR-T-D-C) to improve CAR-T therapy for solid tumors by combining chemotherapy with CAR-T cells, enhancing tumor infiltration and immune response.

The paper introduces TCPGdb, a database integrating T cell perturbation screens to identify critical regulators of T cell function and anti-tumor persistence.

This paper develops a non-viral genome-editing method called STEP-RNPs that effectively treats Angelman syndrome in mice and human neurons, showing functional genetic rescue without off-target effects.

The paper identifies paralog pairs that enhance cancer immunotherapy efficacy through computational analysis and experimental validation.

Perturb-DBiT co-sequences spatial transcriptomes and gRNAs on one tissue section, supporting genome-wide in vivo CRISPR screens in FFPE and fresh-frozen tumors across three cancer models.

The paper identifies actin capping proteins as key modulators of therapeutic responses to radiation and immunotherapy through genome-scale CRISPR screens in melanoma cells.

The paper demonstrates efficient multiplexed gene editing in immune cells using Cas12a/Cpf1 knock-in mice and associated viral vectors.

The paper identifies genetic interactions between cancer mutations and immune resistance genes using a CRISPR double knockout screen, revealing potential targets for existing drugs.

The paper analyzes T cell receptor repertoires from COVID-19 patients and healthy donors to identify signatures associated with disease severity and develops machine learning models to predict severity based on TCR features.

This study examines age-related changes in the human lung transcriptome and cellular composition, linking them to increased COVID-19 severity in older adults.

This study identifies Nsp1 as a key pathogenicity factor of SARS-CoV-2 that redirects host translation machinery to viral RNA by inhibiting cellular protein synthesis and altering gene expression programs.

The paper analyzes new genes in Drosophila to show that recently evolved genes can quickly become essential for development.

The paper uses machine learning to identify pathogenic hotspots in coronavirus genomes, including SARS-CoV-2, and integrates these findings with immunological features to guide vaccine development.

The paper introduces PASTAS, a method for in vivo CRISPR screening in mice, and demonstrates its use in identifying cancer driver variants in liver tumors.

This study uses an AAV-mediated CRISPR screen in mice to identify functional suppressors of glioblastoma, revealing novel and canonical tumor suppressors and their interactions.

The paper uses Cpf1 crRNA arrays to map genetic interactions in vivo, identifying novel drivers and co-drivers of cancer transformation and metastasis.