Direct cDNA preamplification protocols developed for single-cell RNA-seq (scRNA-seq) have enabled transcriptome profiling of  rare cells without having to pool multiple samples or to perform RNA extraction. We term this approach limiting-cell RNA-seq (lcRNA-seq) . Unlike scRNA-seq, which focuses on 'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups. This requires accounting for systems noise which can obscure biological differences. We present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for between-group comparisons. To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at ultralow input levels are known to be noisy. When using CLEAR transcripts vs. using all available transcripts, downstream analyses reveal more shared DEGs , improved Principal Component Analysis separation of cell type, and increased similarity between results across different input RNA amounts. CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house murine neural cell lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cells without sample pooling or RNA extraction. Currently, there is no algorithm optimized to reveal and remove noisy transcripts in limiting-cell RNA-seq (lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed gene (DEG) analysis. Libraries at three input amounts of FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patient were used to develop CLEAR. When using CLEAR transcripts vs. using all transcripts, downstream analyses revealed more shared transcripts across different input RNA amounts , improved Principal Component Analysis (PCA) separation, and yielded more DEGs between cell types. As proof-of-principle, CLEAR was applied to an in-house lcRNA-seq dataset and two public datasets. When imputation is used, CLEAR is also adaptable to large clinical studies and for single cell analyses .
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(scRNA-seq)
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precious clinical samples and
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having to pool multiple samples or to perform
sample pooling or
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We term this approach
Currently, there is no algorithm optimized to reveal and remove noisy transcripts in
R
. Unlike scRNA-seq, which focuses on 'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups. This requires accounting for systems noise which can obscure biological differences. We
data for downstream analyses. Herein, we
R
robust
reliably quantifiable
R
between-group comparisons. To develop CLEAR, we compared DEGs from RNA extracted from
differentially expressed gene (DEG) analysis. Libraries at three input amounts of
D
single
R
diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at ultralow input levels are known to be noisy.
were used to develop CLEAR.
D
available
R
reveal more shared DEGs
revealed more shared transcripts across different input RNA amounts
R
separation of cell type, and increased similarity between results across different input RNA amounts.
(PCA) separation, and yielded more DEGs between cell types. As proof-of-principle,
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two publicly available ultralow input RNA-seq data and
D
murine neural cell
R
. CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells
and two public datasets. When imputation is used, CLEAR is also adaptable to large clinical studies and for single cell analyses