<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Chao Chen</submitter><organism>Thermoplasma acidophilum DSM 1728</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15782</full_dataset_link><description>Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality, and therapies utilizing tumor-infiltrating lymphocytes (TILs) show significant promise. However, the molecular signatures that define a productive TIL-mediated response against tumors remain poorly characterized. Here, we establish a patient-derived organoid and autologous TIL co-culture platform to dissect this interaction at high resolution. We show that expanded TILs mediate potent and specific cytotoxicity against NSCLC organoids. This functional response is associated with a crucial shift in T-cell states, from proliferative towards effector memory phenotypes, and involves the activation of key signaling networks including the Tumor Necrosis Factor (TNF) and Interleukin-17 (IL-17) pathways. Furthermore, analysis of the T-cell receptor (TCR) repertoire confirms that the expansion process selectively enriches tumor-associated clonotypes, resulting in a more focused repertoire. This work delineates the transcriptional and clonal signatures of an effective anti-tumor immune response, providing a robust framework to guide the development of next-generation personalized TIL therapies.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sample Collection - Patient lung cancer samples, including tumor tissues and adjacent normal tissues, were obtained from the Department of Thoracic Surgery at Peking University Shenzhen Hospital. Written informed consent was obtained from all participants prior to sample collection. All procedures were approved by the Ethics Committee of Peking University Shenzhen Hospital (NO.2020-057). Tissues were washed with DPBS containing 1% penicillin/streptomycin. Non-target tissues were manually removed before digestion.</sample_protocol><sample_protocol>Library Construction - Single-cell RNA-seq libraries were prepared using the DNBelab C4 Single-Cell Library Prep Set (MGI) according to the manufacturer's instructions. Following mRNA capture on barcoded beads, emulsion was broken and beads were recovered. Full-length cDNA was synthesized via reverse transcription. The resulting cDNA was then amplified to generate the final single-cell sequencing libraries.</sample_protocol><sample_protocol>Sequencing - The final single-cell libraries were quantified using the Qubit ssDNA Assay Kit (Thermo Fisher Scientific). Sequencing was performed on the DNBSEQ-T7 platform (MGI) at the China National GeneBank (CNGB) using a paired-end sequencing strategy.</sample_protocol><sample_protocol>Nucleic Acid Extraction - Nucleic acid extraction was performed as an integrated part of the single-cell library preparation protocol using the DNBelab C4 Single-Cell Library Prep Set (MGI). Within the microfluidic system, individual cells were encapsulated in droplets with barcoded beads. Cell lysis occurred within the droplets, and poly-adenylated mRNA molecules were captured onto the oligo-dT primers on the surface of the beads.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Single-cell RNA sequencing data analysis was performed using the Seurat (v4.0.6) package in R. After quality control (nFeature_RNA > 500 &amp; &lt; 8000 &amp; percent.mt &lt; 10), data were normalized using the \"LogNormalize\" method. Harmony was used for batch correction. Cell clustering was performed using the Louvain algorithm on the first 20 principal components. DEGs were identified using the \"FindAllMarkers\" function (min.pct = 0.25, logfc.threshold = 0.25) with selection criteria of FDR &lt; 0.01 and |log2FC| > 0.585.</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>DNBSEQ-T7</instrument_platform><study_type>RNA-seq of coding RNA from single cells</study_type><species>Thermoplasma acidophilum DSM 1728</species><pubmed_authors>Chao Chen</pubmed_authors></additional><is_claimable>false</is_claimable><name>Transcriptome Profiling of Tumor-Infiltrating Lymphocyte-Mediated Cytotoxicity against Patient-Derived Lung Cancer Organoids</name><description>Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality, and therapies utilizing tumor-infiltrating lymphocytes (TILs) show significant promise. However, the molecular signatures that define a productive TIL-mediated response against tumors remain poorly characterized. Here, we establish a patient-derived organoid and autologous TIL co-culture platform to dissect this interaction at high resolution. We show that expanded TILs mediate potent and specific cytotoxicity against NSCLC organoids. This functional response is associated with a crucial shift in T-cell states, from proliferative towards effector memory phenotypes, and involves the activation of key signaling networks including the Tumor Necrosis Factor (TNF) and Interleukin-17 (IL-17) pathways. Furthermore, analysis of the T-cell receptor (TCR) repertoire confirms that the expansion process selectively enriches tumor-associated clonotypes, resulting in a more focused repertoire. This work delineates the transcriptional and clonal signatures of an effective anti-tumor immune response, providing a robust framework to guide the development of next-generation personalized TIL therapies.</description><dates><release>2025-10-31T00:00:00Z</release><modification>2026-05-27T17:44:20.892Z</modification><creation>2025-10-20T11:07:34.942Z</creation></dates><accession>E-MTAB-15782</accession><cross_references><ENA>ERP182494</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005684</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>