Project description:Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
Project description:We have developed a high-throughput amplification method for generating robust gene expression profiles using single cell or low RNA inputs.The method uses tagged priming and template-switching, resulting in the incorporation of universal PCR priming sites at both ends of the synthesized cDNA for global PCR amplification. Coupled with a whole-genome gene expression microarray platform, we routinely obtain expression correlation values of R(2)~0.76-0.80 between individual cells and R(2)~0.69 between 50 pg total RNA replicates. Expression profiles generated from single cells or 50 pg total RNA correlate well with that generated with higher input (1 ng total RNA) (R(2)~0.80). Also, the assay is sufficiently sensitive to detect, in a single cell, approximately 63% of the number of genes detected with 1 ng input, with approximately 97% of the genes detected in the single-cell input also detected in the higher input.In summary, our method facilitates whole-genome gene expression profiling in contexts where starting material is extremely limiting, particularly in areas such as the study of progenitor cells in early development and tumor stem cell biology.
Project description:Determining chromatin-associated protein localization across the genome has provided insight into the functions of DNA-binding proteins and their connections to disease. However, established protocols requiring large quantities of cell or tissue samples currently limit applications for clinical and biomedical research in this field. Furthermore, most technologies have been optimized to assess abundant histone protein localization, prohibiting the investigation of nonhistone protein localization in low cell numbers. We recently described a protocol to profile chromatin-associated protein localization in as low as one cell: ultra-low-input cleavage under targets and release using nuclease (uliCUT&RUN). Optimized from chromatin immunocleavage and CUT&RUN, uliCUT&RUN is a tethered enzyme-based protocol that utilizes a combination of recombinant protein, antibody recognition and stringent purification to selectively target proteins of interest and isolate the associated DNA. Performed in native conditions, uliCUT&RUN profiles protein localization to chromatin with low input and high precision. Compared with other profiling technologies, uliCUT&RUN can determine nonhistone protein chromatin occupancies in low cell numbers, permitting the investigation into the molecular functions of a range of DNA-binding proteins within rare samples. From sample preparation to sequencing library submission, the uliCUT&RUN protocol takes <2 d to perform, with the accompanying data analysis timeline dependent on experience level.
Project description:Determining the off-target cleavage profile of programmable nucleases is an important consideration for any genome editing experiment, and a number of Cas9 variants have been reported that improve specificity. We describe here tagmentation-based tag integration site sequencing (TTISS), an efficient, scalable method for analyzing double-strand breaks (DSBs) that we apply in parallel to eight Cas9 variants across 59 targets. Additionally, we generated thousands of other Cas9 variants and screened for variants with enhanced specificity and activity, identifying LZ3 Cas9, a high specificity variant with a unique +1 insertion profile. This comprehensive comparison reveals a general trade-off between Cas9 activity and specificity and provides information about the frequency of generation of +1 insertions, which has implications for correcting frameshift mutations.