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| Item Type: | Dataset |
|---|---|
| Title: | Datasets and Rscripts for : Multiomic and Longitudinal Dissection of Immune Dynamics Associated with Parkinsonism after Ciltacabtagene Autoleucel Therapy |
| Creators Name: | Kadel, Sofie-Katrin, Leipold, Alexander M., Raskó, Tamás and Pande, Amit |
| Abstract: | Seurat Objects ds_index Contains all cells recovered via scRNA-seq and scTCR-seq from cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) of the index patient who developed parkinsonism following CAR-T cell therapy. Samples were collected longitudinally across multiple time points (day 20 to day 204 post–CAR T). T_cells_index Subset of ds_index comprising T cells identified from the combined CSF and PBMC samples of the index patient. ds_control Contains all cells recovered from peripheral blood (PBMCs) via scRNA-seq and scTCR-seq from: the index patient at early time points (pre–CAR T and day 7 post–CAR T), and three additional control patients treated with cilta-cel, sampled at matched time points (pre–CAR T and day 9/10 post–CAR T). T_cells_control Subset of ds_control comprising T cells identified from PBMC samples of the index patient (pre–CAR T and day 7 post–CAR T) and the three control patients treated with cilta-cel (pre–CAR T and day 9/10 post–CAR T). R Scripts Additionally, three R scripts are provided: Script_index_QC_combination_of_index_datasets (Quality Control index patient) Details the quality control, normalization, integration, applied to the index patient datasets. Script_index_analysis_ds_T_cell_subset (Visualization index patient) Contains the code used to generate figures and perform downstream analyses of the index patient dataset, including clonal and phenotypic characterization. Script_control_QC_and_downstream (Early time point and control cohort processing script) Describes the quality control, Seurat object creation, and visualization steps for early time point samples (ds_control and T_cells_control) from the index patient and the control cohort. Methods Summary for Lentiviral Integration Analysis Data Preprocessing: Adapter Removal and Quality Trimming Raw sequencing reads were processed using cutadapt to remove adapter sequences. Further trimming for low-quality bases and length filtering was performed using Trimmomatic with the parameters SLIDINGWINDOW:4:20 and MINLEN:50. Filter Out CAR Construct Reads Reads containing the CAR construct sequence were identified and removed using grep and seqtk to ensure only genomic sequences of interest remained for further analysis. Mapping and Alignment Genome Reference Construction A custom genome reference (hg19_lenti.fa) was built by appending the lentiviral sequence to the human genome (hg19) reference. Alignment Trimmed reads were aligned to the custom reference genome using BWA-MEM version 0.7.17 for high sensitivity. SAMtools v1.21 was used to convert and sort alignment files for downstream processing. Identification of Chimeric Reads Chimeric reads (spanning human and lentiviral integration sites) were extracted using samtools and processed further. Integration Site Analysis Annotation of Integration Sites Significant integration sites were determined using BEDTools v2.31 to identify overlap with genomic features. Annotated integration sites were further processed using HOMER v5.1 to classify insertions into genomic regions (e.g., promoters, introns, exons, intergenic regions). Visualization and Results Chromosomal Distribution Integration site distributions were visualized across chromosomes, excluding haploid content using a python script. Feature Enrichment Enrichment analysis for genomic features (e.g., TEs, promoters) was performed using observed-to-expected ratios and log2 enrichment scores, using HOMER. Summary This workflow allowed us to identify approximately 19,046 unique lentiviral integration sites from a population of ~1 million cells. The analysis confirmed the preference of lentiviral vectors for active genomic regions, including transcriptional elements and repetitive sequences. |
| Source: | Zenodo |
| Publisher: | CERN |
| Date: | 2026 |
| Official Publication: | https://doi.org/10.5281/zenodo.19072073 |
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