Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies.
Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies. Developing new sequencing technology LCM-seq to efficiently sequence mouse and human tissues
Project description:The first GSSM of V. vinifera was reconstructed (MODEL2408120001). Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases.
Project description:Gene expression profiles of specific neuronal populations might explain differential vulnerability to neurodegeneration in the lethal disease amyotrophic lateral sclerosis (ALS). Using laser capture microscopy (LCM) and RNA sequencing (LCM-seq), we demonstrate that the molecular signature of degeneration-resistant oculomotor neurons (OMNs) is distinct from that of vulnerable spinal motor neurons (MNs).
Project description:We describe the use of laser capture microdissection (LCM) to isolate human and murine sebaceous glands (SGs) for transcriptomic analysis and publish this SG transcriptomic data for reference. We show that compared to whole skin RNA sequencing, LCM RNA sequencing allows for high resolution in identifying and describing SG genes at homeostasis. Lastly, we compare this LCM of sebaceous glands to published SG clusters from single cell RNA sequencing of skin, and show that we achieve greater resolution and depth with the LCM approach.
