Similar Datasets
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.
2016-07-05 | GSE76514 | GEO
Project description:This model was reconstructed from single-nucleus RNA-seq (snRNA-seq) data of human postmortem brain and curated using published metabolomics data from human iPSC-derived neurons and cerebrospinal fluid (CSF), together with gene expression data from the Human Protein Atlas. It more accurately simulates human neuronal metabolic flux in neurodegenerative conditions such as Alzheimer's disease (AD).
2026-06-27 | MODEL2606170001 | BioModels
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
2016-07-05 | E-GEOD-76514 | biostudies-arrayexpress
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).
2020-02-14 | GSE115130 | GEO
Project description:p48Cre LSL-KrasG12D Mouse Acinar and PanIN LCM RNA-Seq
| PRJNA1177900 | ENA