Project description:We developed SLIC-CAGE (Super-Low Input Carrier-CAGE) approach to capture 5'end of RNA polymerase II transcripts from as little as 5-10 ng of total RNA. The dramatic increase in sensitivity compared to existing CAGE methods is achieved by specially designed, selectively degradable carrier RNA. We apply SLIC-CAGE on mouse primordial germ cells embryonic day (E) 11.5 - 2 biological replicates.
Project description:BACKGROUND:Transcriptional profiling with ultra-low input methods can yield valuable insights into disease, particularly when applied to the study of immune cells using RNA-sequencing. The advent of these methods has allowed for their use in profiling cells collected in clinical trials and other studies that involve the coordination of human-derived material. To date, few studies have sought to quantify what effects that collection and handling of this material can have on resulting data. RESULTS:We characterized the global effects of blood handling, methods for leukocyte isolation, and preservation media on low numbers of immune cells isolated from blood. We found overall that storage/shipping temperature of blood prior to leukocyte isolation and sorting led to global changes in both CD8+ T cells and monocytes, including alterations in immune-related gene sets. We found that the use of a leukocyte filtration system minimized these alterations and we applied this method to generate high-quality transcriptional data from sorted immune cells isolated from the blood of intracerebral hemorrhage patients and matched healthy controls. CONCLUSIONS:Our data underscore the necessity of processing samples with comparably defined protocols prior to transcriptional profiling and demonstrate that a filtration method can be applied to quickly isolate immune cells of interest while minimizing transcriptional bias.
Project description:Ultimately, cell biology seeks to define molecular mechanisms underlying cellular functions. However, heterogeneity within cell populations must be considered for optimal assay design and data interpretation. Although single-cell analyses are desirable for addressing this issue, practical considerations, including assay sensitivity, limit their broad application. Therefore, omics studies on small numbers of cells in defined subpopulations represent a viable alternative for elucidating cell functions at the molecular level. MS-based proteomics allows in-depth proteome exploration, although analyses of small numbers of cells have not been pursued due to loss during the multistep procedure involved. Thus, optimization of the proteomics workflow to facilitate the analysis of rare cells would be useful. Here, we report a microproteomics workflow for limited numbers of immune cells using non-damaging, microfluidic chip-based cell sorting and MS-based proteomics. Samples of 1000 or 100 THP-1 cells were sorted, and after enzymatic digestion, peptide mixtures were subjected to nano-LC-MS analysis. We achieved reasonable proteome coverage from as few as 100-sorted cells, and the data obtained from 1000-sorted cells were as comprehensive as those obtained using 1 ?g of whole cell lysate. With further refinement, our approach could be useful for studying cell subpopulations or limited samples, such as clinical specimens.
Project description:In order to characterize the effects of upstream sample handling on the transcriptome of isolated leukocyte populations, we simulated various sample handling methods on whole blood prior to leukocyte isolation for low-input RNA-sequencing.
Project description:Mesenchymal stem cells (MSCs) have regenerative properties, but recently they were also found to have immunomodulatory capacities. We therefore investigated whether MSCs could reduce atherosclerosis, which is determined by dyslipidaemia and chronic inflammation. We adoptively transferred MSCs into low-density lipoprotein-receptor knockout mice and put these on a Western-type diet to induce atherosclerosis. Initially after treatment, we found higher levels of circulating regulatory T cells. In the long-term, overall numbers of effector T cells were reduced by MSC treatment. Moreover, MSC-treated mice displayed a significant 33% reduction in circulating monocytes and a 77% reduction of serum CCL2 levels. Most strikingly, we found a previously unappreciated effect on lipid metabolism. Serum cholesterol was reduced by 33%, due to reduced very low-density lipoprotein levels, likely a result of reduced de novo hepatic lipogenesis as determined by a reduced expression of Stearoyl-CoA desaturase-1 and lipoprotein lipase. MSCs significantly affected lesion development, which was reduced by 33% in the aortic root. These lesions contained 56% less macrophages and showed a 61% reduction in T cell numbers. We show here for the first time that MSC treatment affects not only inflammatory responses but also significantly reduces dyslipidaemia in mice. This makes MSCs a potent candidate for atherosclerosis therapies.
Project description:BACKGROUND: Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) offers high resolution, genome-wide analysis of DNA-protein interactions. However, current standard methods require abundant starting material in the range of 1-20 million cells per immunoprecipitation, and remain a bottleneck to the acquisition of biologically relevant epigenetic data. Using a ChIP-seq protocol optimised for low cell numbers (down to 100,000 cells/IP), we examined the performance of the ChIP-seq technique on a series of decreasing cell numbers. RESULTS: We present an enhanced native ChIP-seq method tailored to low cell numbers that represents a 200-fold reduction in input requirements over existing protocols. The protocol was tested over a range of starting cell numbers covering three orders of magnitude, enabling determination of the lower limit of the technique. At low input cell numbers, increased levels of unmapped and duplicate reads reduce the number of unique reads generated, and can drive up sequencing costs and affect sensitivity if ChIP is attempted from too few cells. CONCLUSIONS: The optimised method presented here considerably reduces the input requirements for performing native ChIP-seq. It extends the applicability of the technique to isolated primary cells and rare cell populations (e.g. biobank samples, stem cells), and in many cases will alleviate the need for cell culture and any associated alteration of epigenetic marks. However, this study highlights a challenge inherent to ChIP-seq from low cell numbers: as cell input numbers fall, levels of unmapped sequence reads and PCR-generated duplicate reads rise. We discuss a number of solutions to overcome the effects of reducing cell number that may aid further improvements to ChIP performance.
Project description:Chromosome conformation capture (3C) techniques are crucial to understanding tissue-specific regulation of gene expression, but current methods generally require large numbers of cells. This protocol describes two new low-input Capture-C approaches that can generate high-quality 3C interaction profiles from 10,000-20,000 cells, depending on the resolution used for analysis.