Vampirium setup This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github: Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline. Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology. Slides.zip contains all the slides used in the workshop. raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.

This archive contians datasets, exercises and slides used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course material can be found on Github.

Data.zip contains fastqc and multiqc examples of bulk RNAseq experiments, plus count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata.

Slides.zip contains all the slides used in the workshop.

In order to identify the transcriptional changes that correlate with kidney regeneration or fibrosis development, we performed a time-course bulk RNA-seq from whole-kidneys at 3, 7, 14, 28 and 42 days after the initial ischemic kidney from two distinct murine models.

This is the PBMC training dataset used for training Scaden models to perform deconvolution on PBMC RNA-seq datasets. It is compiled from four different PBMC scRNA-seq datasets downloaded from the 10X Genomics website (donorA, donorC, data6k, data8k).The datasets downloaded from 10X Genomics were processed and used to generate artificial bulk RNA-seq samples, which result in this dataset. A link to the 10X Genomics datasets site is provided.

Source data and scripts associated with the manuscript "Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols" (Naas et al. 2024, bioRxiv, DOI: 10.1101/2024.11.15.623576). Corresponding scripts to reproduce all figures and tables presented in the manuscript are also available on https://github.com/jn-goe/brain_organoids_four_protocols.

Additional file 5: CSV file of bulk RNA-seq data of F. nucleatum infection time course used for GECO UMAP generation.

Overview

A permanent archive of the datasets used in the CellO manuscript (https://www.biorxiv.org/content/10.1101/634097v2).

Expression data

• Quantified gene expression for all bulk RNA-seq samples used in this study are available as an HDF5 file (in log transcripts per million): bulk_log_tpm.h5
• Each bulk RNA-seq experiment accession is mapped to a set of cell type labels from the Cell Ontology: bulk_labels.json
• The single-cell data used in this study are also available as an HDF5 file (in log transcripts per million): single_cell_log_tpm.h5
• Each single-cell experiment accession is mapped to a set of cell type labels from the Cell Ontology: single_cell_labels.json

Dataset partitions

We partitioned the bulk RNA-seq data into several subsets that were used for various purposes in the study:

• The list of bulk RNA-seq samples used for training the classifier for evaluation on the bulk validation-set (i.e. the pre-taining set): training_bulk_experiments.json
• The list of bulk RNA-seq samples in the validation-set: test_bulk_experiments.json
• The list of single-cell experiments in the test set used for evaluting CellO. These are all samples with cell type terms that also appear in the bulk RNA-seq data (i.e. the training data): test_single_cell_experiments.json

Technical variable annotations

We annotated 27,097 RNA-seq samples in the Sequence Read Archive (SRA) with technical variables in order to derive a set of primary, healthy, untreated samples (i.e. the datasets above).

• Our annotations were based on a custom label-hierarchy of technical variables: tags.json
• The mapping from each SRA experiment accession to its set of technical variable labels: experiment_tags.json

Trained model coefficients

After training the binary classifiers for each cell type, the model coefficients can be used to investigate up and downregulated genes in each cell type. Below, we post the model coefficients for the one-versus-rest trained binary classifiers (used in the Isotonic Regression and True Path Rule algorithms) as well as the coefficients for the classifiers in the Cascaded Logistic Regression algorithm. Each model was trained on the full set of bulk RNA-seq samples used in the study. Each algorithm's cell type model coefficients are available in a tab-separated-value file:

• One-versus-rest classifier coefficients: one_vs_rest_coefficients.tsv.gz
• Cascaded logistic regression coefficients: cascaded_logistic_regression_coefficients.tsv.gz

Mouse brain training dataset. This dataset was created by simulating artificial bulk RNA-seq samples from five different scRNA-seq datsets:Dataset 1: Campbel et al., Nat. Neurosci. 2017Dataset 2: Chen et al., Cell Reports 2017Dataset 3: Tasic et al., Nat. Neurosci., 2016Dataset 4: Romanov et al., Nat. Neurosci. 2017Dataset 5: Zeisel et al., Science, 2015Links to these publications are provided in the references.

This dataset contains single-cell RNA sequencing and time-course bulk RNA sequencing data of brain organoids grown from multiple cell lines using four different protocols recapitulating dorsal and ventral forebrain, midbrain, and striatum.

This gtf has been generated based on https://doi.org/10.5281/zenodo.7510406 and extends 3' of genes using RefSeq and bulk RNA-seq from GSE106225, GSE113885 and time-course samples from GSE205781. All command lines can be found at https://github.com/lldelisle/extendMouseGTFUsingGastruloidData.

Bispecific T-cell engagers (BiTEs) have demonstrated remarkable success in treating B cell precursor acute lymphoblastic leukemia, B cell non-Hodgkin’s lymphoma, and multiple myeloma. However, their efficacy in acute myeloid leukemia (AML) has so far been limited. Emerging evidence suggests that BiTE therapy may be more effective in the setting of low tumor burden or minimal residual disease, e.g., in between chemotherapy courses. Yet, it is unclear whether current chemotherapy-based regimens create a bone marrow (BM) microenvironment conducive to BiTE therapy. To address this gap, we performed a comprehensive analysis of the BM lymphocyte compartment in newly diagnosed pediatric AML patients undergoing induction therapy across three international treatment protocols. We observed significant differences in BM lymphocyte frequencies over the course of therapy, with distinct dynamics across protocols. Additionally, we demonstrated that T-cells from pediatric AML patients obtained at end-of-induction one and two are functional and responsive to BiTE therapy. These findings highlight the importance of systematically characterizing the BM immune microenvironment during chemotherapy-based regimens to identify the optimal timing for BiTE therapy. Furthermore, they provide a compelling rationale for exploring the potential of BiTE therapy in between chemotherapy courses in pediatric AML.

For this study, we acquired diagnostic bulk RNA-sequencing data generated from bone marrow mononuclear cells (BMMCs) from children with acute myeloid leukemia (AML) at diagnosis (n=51). We also performed bulk RNA-sequencing to generate data for time points in between chemotherapy courses: end of induction 1 (n=21), end of induction 2 (n=21), and end of consolidation (n=6). As a reference, we used and/or conducted bulk RNA-sequencing (data) from age-matched children that did not have leukemia (non-leukemic controls: n=4 were previously published by our group (early-stage rhabdomyosarcoma without bone marrow involvement), PMID=37908360; n=3 were newly generated (healthy siblings of patients undergoing allogeneic hematopoietic stem cell transplantation)). Briefly, total RNA was isolated from BMMCs, and RNA-seq libraries were generated from 300 ng RNA, and sequenced using the NovaSeq 6000 (2x150 bp; Illumina). After pre-processing, RNA-sequencing typically yielded 60-100 million raw reads, which were then aligned to the GRCh38 reference genome using the gene annotations provided by GENCODE version 31. Raw counts were normalized to counts per million (CPM) and used with CIBERSORTx (cibersortx.stanford.edu)and a healthy BM reference, to infer the abundance of lymphoid subsets in the BM.

See the metadata sheet for more details on the methods.

We report the number of nonlinear dynamic eGenes (genes with a significant nonlinear dynamic eQTL at gene-level q-value < = 0.05), for each of three aggregation schemes assessed. Total number of genes tested and total number of tests run are also reported. (XLSX)