Project description:Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from the transcriptome. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2), which builds on our earlier efforts to combine single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrated the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.
Project description:Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor, and surgical resection is a key part of the standard-of-care. In fluorescence-guided surgery (FGS), fluorophores are used to differentiate tumor tissue from surrounding normal brain. The heme synthesis pathway converts 5-aminolevulinic acid (5-ALA), a fluorogenic substrate, to the fluorophore protoporphyrin IX (PpIX). The resulting fluorescence is thought to be specific to transformed glioma cells, but this specificity has not been examined at single cell level. Here, we performed paired single cell imaging and RNA sequencing of individual cells (SCOPE-seq2) on human GBM surgical specimens with visible PpIX fluorescence from patients who received 5-ALA prior to surgery. SCOPE-seq2 allows us to simultaneously measure PpIX fluorescence by imaging and unambiguously identify transformed glioma cells from single-cell RNA-seq (scRNA-seq). We observed that 5-ALA treatment results in labeling that is not specific to transformed tumor cells. In cell culture experiments, we further demonstrated untransformed cells can be labeled by 5-ALA directly or by PpIX secreted from surrounding transformed cells. In acute slice cultures from mouse glioma models, we showed that 5-ALA preferably labels GBM tumor tissue over non-neoplastic brain tissue at bulk level, and that this contrast is not due to blood-brain-barrier disruption. Taken together, our findings support the use of 5-ALA as an indicator of GBM tissue, but not as a specific marker of transformed glioma cells.
Project description:Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with three-fold higher sensitivity, lower costs, and less hands-on time. We also implemented CEL-Seq2 on Fluidigm’s C1 system, thereby providing its first single-cell, on-chip barcoding method, and detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use
Project description:Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artificial reads, enhanced throughput, and increased library complexity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large scale single-cell epigenomic studies.
Project description:Purpose: we propose Sequence-Scope (Seq-Scope), which can generate ultra-high definition images of sequence-based molecular signatures resolved at a submicrometer scale. Experimental Methods: Seq-Scope experiment is divided into two consecutive sequencing steps: 1st-Seq and 2nd-Seq. 1st-Seq of Seq-Scope starts with the solid-phase amplification of a single-stranded synthetic oligonucleotide library using an Illumina sequencing-by-synthesis (SBS) platform. 2nd-Seq of Seq-scope begins with overlaying the tissue section slice onto the HDMI-array. Computational Methods: Tissue boundaries are detected by using a custom python code to draw a smoothed density plot to visualize the density of HiSeq reads in a given XY space of each tile. Digital gene expression (DGE) matrices are generate using STAR/STARsolo 2.7.5c with Gene,GeneFull, Velocyto, and polyAtrimming options. Data binning is performed by dividing the imaging space into 100 μm2 square grid with 10 μm simple side or 25 μm2 square grid with 5 μm side and collapsing all HDMI-UMI information into one barcode. Binned DGE matrix was analyzed in the Seurat v4 package for clustering analysis.
Project description:Optically decodable beads link the identity of an analyte or sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We have combined microfluidics with optically decodable beads to link phenotypic analysis of living cells to sequencing. As a proof-of-concept, we applied this to demonstrate an accurate and scalable tool for connecting live cell imaging to single-cell RNA-Seq called Single Cell Optical Phenotyping and Expression (SCOPE-Seq).
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in several of the reaction steps of Quartz-Seq2 allow us to effectively convert initial reads to UMI counts (at a rate of 30%–50%). To demonstrate the power of Quartz-Seq2, we analyzed transcriptomes from a cell population of in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of sequence reads. Preprint: http://www.biorxiv.org/content/early/2017/07/21/159384
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform a replicate of Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit