Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE129240 | GEO | 2019/04/03
REPOSITORIES: GEO
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Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. Its ability to discriminate related human cells is unknown. Here we test the ability of this technique to determine the fractions of transformed cells of immune origin in mixed samples. Experiment Overall Design: Four immune cell lines were grown and run on microarrays either by themselves or in mixtures of various relative proportions. Mixtures of cells were performed in triplicate. Experiment Overall Design: MixA (Jurkat: 2.5, IM-9: 1.25, Raji: 2.5, THP-1: 3.75) Experiment Overall Design: MixB (Jurkat: 0.5, IM-9: 3.17, Raji: 4.75, THP-1: 1.58) Experiment Overall Design: MixC (Jurkat: 0.1, IM-9: 4.95, Raji: 1.65, THP-1: 3.3) Experiment Overall Design: MixD (Jurkat: 0.02, IM-9: 3.33, Raji: 3.33, THP-1: 3.33)
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Project description:These experiments were designed as a benchmark tool for deconvolution methods. 5 immune cell populations were sorted from 3 healthy donors' peripheral bloods. Peripheral Blood Mononuclear Cells (PBCMs) and PolymorphoNuclear Cells (PMN) were separated using gradient centrifugation. T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-) and NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+) were FACS-sorted from PBMCs and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were sorted from PMNs. RNA was extracted from the purified cell population, as well as from the HCT116 colon cancer cell line. RNAs from pure populations were then mixed in various proportions. RNA from HCT116 cells and FACS-sorted T cells (DAPI-/CD3+/CD14-/CD19-/CD56-), monocytes (DAPI-/CD3-/CD14+/CD19-/CD56-), B cells (DAPI-/CD3-/CD14-/CD19+/CD56-), NK cells (DAPI-/CD3-/CD14-/CD19-/CD56+), and neutrophils (DAPI-/CD66b+/CD19-/CD3-/CD56-/CD14-) were mixed in various proportions.
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Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
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Project description:Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures
| PRJNA530575 | ENA
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
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