Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA-Seq of 501mel and WM-266-4 melanoma cell lines transplanted into mice

ABSTRACT: Melanoma is notorious for its phenotype heterogeneity, which poses a great challenge for current therapies. Two major melanoma-phenotypes are represented by ‘non-EMT-like’ MITFhigh and ‘EMT-like’ MITFlow subpopulations, and while it is thought that transcriptional plasticity leading to a switch from one phenotype to the other drives melanoma progression, recent findings suggest that phenotype switching might not be so clear-cut in vivo. To understand the impact of MITFhigh and MITFlow cells on each other within a heterogeneous tumour we sorted the individual populations from homogeneous and heterogeneous tumours and performed RNAseq. We analysed MITFhigh and MITFlow subpopulation throughout melanoma progression and found no clear ‘switch’, but rather an ‘adaptation’ with an overlap of phenotype-defining signatures during different stages of melanoma development. Overall, when compared to homogeneous tumours, heterogeneous tumours displayed major changes in ECM-interactions with increased expression of FN1, TNC and matrix re-modellers, and alterations in the repertoire of collagens and adhesion receptors

INSTRUMENT(S): Illumina HiSeq 4000

ORGANISM(S): Homo sapiens

SUBMITTER: Leo Zeef

PROVIDER: E-MTAB-7377 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:It is widely accepted that complex interactions between cancer cells and their surrounding microenvironment contribute to disease development, chemo-resistance and disease relapse. In light of this observed interdependency, novel therapeutic interventions that target specific cancer stroma cell lineages and their interactions are being sought. To this end, we studied a mouse model of human T cell acute lymphoblastic leukaemia (T-ALL) and used intravital microscopy to monitor the progression of disease within the bone marrow at both the tissue-wide and single cell level over time. We observed highly dynamic cellular interactions and promiscuous distribution of leukaemia cells that migrated across, and proliferated within multiple bone marrow sub-compartments. Unexpectedly, this environment-agnostic behaviour was maintained throughout disease development, starting from the earliest bone marrow seeding to the response and resistance to chemotherapy. Our results reveal that T-ALL cells do not depend on specific bone marrow microenvironments for propagation of disease, nor for the selection of chemo-resistant clones, suggesting a stochastic mechanism underlies these processes. Yet, while T-ALL infiltration and progression are niche-independent, accumulated disease burden leads to rapid, selective remodelling of the endosteal space, resulting in a complete loss of mature osteoblastic cells whilst perivascular cells are maintained. This outcome leads to a shift in the balance of endogenous bone marrow stroma, towards a composition associated with less efficient haematopoietic stem cell function1. This novel, dynamic analysis of T-ALL interactions with the bone marrow microenvironment in vivo highlights that future therapeutic interventions should target cancer cell-intrinsic mechanisms, rather than signals from specific bone marrow stroma, in order to combat the invasion by and survival of chemo-resistant T-ALL cells. Notch-1 T-ALL tumours were generated as previously described in (Hawkins et al., PLoS One, 2014 - doi: 10.1371/journal.pone.0087376.).The T-ALL mouse model was generated by retroviral transduction of C57Bl/6 E14.5 fetal liver cells as described in (Hawkins et al., PLoS One, 2014 - doi: 10.1371/journal.pone.0087376.). Whole transduced populations were transplanted into 8-14 week old female C57Bl/6 recipient mice. T cell acute lymphoblastic leukemia cells were positive for the fluorescent protein (GFP or DsRed) indicating expression of the Notch-1 intracellular protein. These tumours were most frequently CD4+8+ as originally described in (Aster, J. C. et al. Essential roles for ankyrin repeat and transactivation domains in induction of T-cell leukemia by notch1. Molecular and cellular biology 20, 7505-7515 (2000). Disease was confirmed by flow cytometry and 2-photon imaging. Mice reconstituted with Notch transduced foetal livers were monitored daily for signs of leukaemia onset or other signs of ill health. Tumours were harvested from bone marrow once full infiltration was established as confirmed by peripheral blood and bone marrow flow cytometry as well as 2-photon imaging. For treated samples, dexamethasone (Dexamethasone Sodium Phosphate - Sigma) was administered daily I.V. - 15mg/kg for 7 days. Tumours were isolated purified from bone marrow by FACS sorting based on expression of fluorescent protein expression (GFP+ or DsRed+).

Project description:scRNA-seq procedure and analysis. ScRNA-seq was performed using the Massively Parallel Single-Cell RNA-sequencing technology (MARS-seq) as described by Jaitin et al., 2014 (PMID: 24531970) EpiSCs were seeded at ~6 x 103 cells/cm2 in 6-well cell-culture plates pre-coated with 15 µg/ml human Fibronectin, and differentiated to APSD. At desired time-points, cells were dissociated using Accutase (Thermo Fisher Scientific, 00-4555-56) for 3 min at 37°C and counted. 500,000 cells per condition were washed with ice-cold FACS buffer (10% (v/v) FBS in PBS) and resuspended into 1 ml ice-cold FACS buffer containing 1 µg/ml DAPI. For in vivo experiments, E6.5/E7.0/E7.75 Tg(Eomes::GFP) BAC transgenic embryos were individually dissociated into 300 μl warmed Trypsin 0.05% (w/v) EDTA for 10 min at 37°C. Digestion was stopped by addition of 600 μl ice-cold FACS buffer followed by centrifugation at 350 x g for 3 min. Cells were washed with 500 μl ice-cold FACS buffer, centrifuged again at 350 x g for 3 min and resuspended into 250 μl ice-cold FACS buffer containing 1 µg/ml DAPI. Single-cells from either in vitro-differentiated cells or transgenic embryos were sorted into Eppendorf Polypropylene U-shaped 384-well Twin Tec PCR Microplates (Thermo Fisher Scientific, 10573035), containing 2 μl of lysis solution (0.2% (v/v) Triton X-100) supplemented with 0.4 U/μl RNasin Ribonuclease Inhibitor (Promega, N2515) and 400 nM indexed RT primer from group 1 (1-96 barcodes) or group 2 (97-192 barcodes), as described in Jaitin et al., 2014. Additionally, 71 WT EpiSCs were sorted into each plate, as spike-in control for batch-effect correction. Capture plates were prepared on the Bravo automated liquid handling robot station (Agilent) using 384-filtered tips (Axygen, 302-82-101). Index sorting was performed using either a FACS Aria III cell sorter (BD Biosciences) or a SONY SH800S cell sorter (Sony Biotechnology) at the DanStem/reNEW Flow Cytometry Platform (University of Copenhagen, Copenhagen, Denmark), gating in SSC-A versus FSC-A to collect live cells, and then in FSC-W versus FSC-A to sort only singlets. For in vivo experiments, only GFPpos cells were sorted to capture Eomes-expressing cell types. Immediately after sorting, plates were spun down, snap-frozen on dry ice, and stored at -80°C until further processing. Semi-automated library preparation was performed as described by Jaitin et al., 2014, using 10-12 cycles of PCR amplification and AMPure XP beads (Agencourt) for purification. DNA concentration was measured with a Qubit Fluorometer (Thermo Fisher Scientific, Q32854), and fragment size was determined with a Fragment analyzer (Advanced Analytical). Libraries were paired-end sequenced on a Next-Seq 500 Sequencer (Illumina) at the DanStem/reNEW Genomics Platform (University of Copenhagen, Copenhagen, Denmark). Between 1,146 and 1,528 cells were sequenced per lane. R1 and R2 fastq files were generated using bcl2fastq (v2.19.1), and the pooling and well information were extracted from the sequence using umis (v1.0.3) [https://github.com/vals/umis] into a unique fastq file. The reads were then filtered based on the pooling barcodes with 1 mismatch allowed. The poly-Ts at the end of the reads were trimmed using Cutadapt (v1.18). The reads were mapped to the mouse genome (GRCm38/mm10 together with ERCC92) using HISAT2 (v2.1.0), the alignments were processed with Samtools (v1.7)118, and the reads were counted with featureCounts (Subread (v1.5.3)) using Ensembl v93, and the UMIs using UMI_tools (v1.0.0). Expression data were analyzed using Seurat (v3.1). Data filtering, normalization, and scaling were performed using the standard pre-processing workflow. Integration of different datasets was performed as described (PMID: 31178118). Spiked-in EpiSCs were used as a reference to correct the batch effect between integrated datasets. Marker genes of each cell cluster were outputted for GO-term analysis to define the cell type. The Monocle package (v2.16.0) was used to perform pseudotime analyses.

Dataset Information

RNA-Seq of 501mel and WM-266-4 melanoma cell lines transplanted into mice

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets